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Published: 10 December 2022
Last updated: 29 January 2023

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1

Zhang, Tianning, Xingqi Wang, Dan Wei, and Jinglong Fang. "Test Case Prioritization Technique Based on Error Probability and Severity of UML Models." International Journal of Software Engineering and Knowledge Engineering 28, no. 06 (June 2018): 831–44. http://dx.doi.org/10.1142/s0218194018500249.

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Test case prioritization is one of the most useful activities in testing. Most existing test case prioritization techniques are based on code coverage, which requires access to source code. However, code-based testing comes late in the software development life cycle, when errors are detected, the cost of testing is very high. Therefore, in this paper, we provide a test case prioritization technique based on Unified Modeling Language (UML) model, built before coding, to detect errors as earlier as possible and reduce the cost of modification. The technique consists of the following main parts: (1) using C&K metrics to estimate the error probability of class; (2) using dependences, obtained from the model slicing, to estimate error severity; (3) generating test case priority from error probability and severity, then prioritizing the test case. With our technique, test engineers need the UML model only and the test cases can be prioritized automatically. To evaluate our technique, we applied our technique to unmanned aerial vehicles (UAV) flight control system and performed test case prioritization. The results show that the error can be detected effectively and stability can be increased significantly as compared to the current code-based techniques.
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2

KRISHNAMOORTHI, R., and S. A. SAHAAYA ARUL MARY. "REQUIREMENT BASED SYSTEM TEST CASE PRIORITIZATION OF NEW AND REGRESSION TEST CASES." International Journal of Software Engineering and Knowledge Engineering 19, no. 03 (May 2009): 453–75. http://dx.doi.org/10.1142/s0218194009004222.

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Test case prioritization schedules the test cases in an order that increases the effectiveness in achieving some performance goals. One of the most important performance goals is the rate of fault detection. Test cases should run in an order that increases the possibility of fault detection and also detects the most severe faults at the earliest in its testing life cycle. Test case prioritization techniques have proved to be beneficial for improving regression testing activities. While code coverage based prioritization techniques are found to be studied by most scholars, test case prioritization based on requirements in a cost effective manner has not been used for studies so far. Hence, in this paper, we propose to put forth a model for system level Test Case Prioritization (TCP) from software requirement specification to improve user satisfaction with quality software that can also be cost effective and to improve the rate of severe fault detection. The proposed model prioritizes the system test cases based on six factors: customer priority, changes in requirement, implementation complexity, usability, application flow and fault impact. The proposed prioritization technique is experimented in three phases with student projects and two sets of industrial projects and the results show convincingly that the proposed prioritization technique improves the rate of severe fault detection.
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Hudaib, Amjad, Raja Masadeh, Mais Haj Qasem, and Abdullah Alzaqebah. "Requirements Prioritization Techniques Comparison." Modern Applied Science 12, no. 2 (January 15, 2018): 62. http://dx.doi.org/10.5539/mas.v12n2p62.

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Requirements prioritization is considered as one of the most important approaches in the requirement engineering process. Requirements prioritization is used to define the ordering or schedule for executing requirement based on their priority or importance with respect to stakeholders’ viewpoints. Many prioritization techniques for requirement have been proposed by researchers, and there is no single technique can be used for all projects types. In this paper we give an overview of the requirement process and requirement prioritization concept. We also present the most popular techniques used to prioritize the software project requirements and a compression between these techniques. On the other hand, we spot the light on the importance of involving the non-functional requirements prioritization because of the great effects of non-functional on project success and quality; some approaches that used in prioritize non-functional requirements are discussed in this paper, in addition a general model is proposed based on reviewing the prioritization techniques in order to suggests a best suited technique for specific projects according to decision makers parameters.
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Kumar, Harish, and Naresh Chauhan. "A Module Coupling Slice Based Test Case Prioritization Technique." International Journal of Modern Education and Computer Science 7, no. 7 (July 8, 2015): 8–16. http://dx.doi.org/10.5815/ijmecs.2015.07.02.

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Chen, Lizhe, Xiang Yu, Ji Wu, and Haiyan Yang. "CIPC: A Change Impact Propagation Computing Based Technique for Microservice Regression Testing Prioritization." Mobile Information Systems 2021 (November 23, 2021): 1–13. http://dx.doi.org/10.1155/2021/2912240.

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Regression testing is the optimal technique that can be used in each iteration of microservice systems. However, regression testing prioritization is the only main method that gives better results. These techniques directly involve the processes of artifacts, data acquisition, analysis, and maintenance. The microservice systems have input data, which are difficult to obtain and control, while such processes are of high costs with impractical design. This paper gives a detailed study on testing prioritization technique, which is referred to as CIPC. As there are dependencies between services from API gateway logs, a novel CIPC algorithm is proposed, which is based on belief propagation. There are some rules that are directly affected by service changes. Therefore, the higher execution order of test case prioritizes CIPC, which is based on impact changes. Multiobjective prioritization algorithm is based on heuristic searching, in which sequence test cases are done by coverage. By evaluating the effectiveness of CIPC, the empirical study presents five microservice systems and four different techniques. The results describe that CIPC has improved fault detection rate with acceptable time and cost. The technique is more practical than typical artifacts, which are based on increments of system scales.
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WANG, ZIYUAN, LIN CHEN, BAOWEN XU, and YAN HUANG. "COST-COGNIZANT COMBINATORIAL TEST CASE PRIORITIZATION." International Journal of Software Engineering and Knowledge Engineering 21, no. 06 (September 2011): 829–54. http://dx.doi.org/10.1142/s0218194011005499.

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Combinatorial testing has been widely used in practice. People usually assume all test cases in combinatorial test suite will run completely. However, in many scenarios where combinatorial testing is needed, for example the regression testing, the entire combinatorial test suite is not run completely as a result of test resource constraints. To improve the efficiency of testing, combinatorial test case prioritization technique is required. For the scenario of regression testing, this paper proposes a new cost-cognizant combinatorial test case prioritization technique, which takes both combination weights and test costs into account. Here we propose a series of metrics with physical meaning, which assess the combinatorial coverage efficiency of test suite, to guide the prioritization of combinatorial test cases. And two heuristic test case prioritization algorithms, which are based on total and additional techniques respectively, are utilized in our technique. Simulation experimental results illustrate some properties and advantages of proposed technique.
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Mei, Lijun, Yan Cai, Changjiang Jia, Bo Jiang, and W. K. Chan. "Test Pair Selection for Test Case Prioritization in Regression Testing for WS-BPEL Programs." International Journal of Web Services Research 10, no. 1 (January 2013): 73–102. http://dx.doi.org/10.4018/jwsr.2013010104.

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Many web services not only communicate through XML-based messages, but also may dynamically modify their behaviors by applying different interpretations on XML messages through updating the associated XML Schemas or XML-based interface specifications. Such artifacts are usually complex, allowing XML-based messages conforming to these specifications structurally complex. Testing should cost-effectively cover all scenarios. Test case prioritization is a dimension of regression testing that assures a program from unintended modifications by reordering the test cases within a test suite. However, many existing test case prioritization techniques for regression testing treat test cases of different complexity generically. In this paper, the authors exploit the insights on the structural similarity of XML-based artifacts between test cases in both static and dynamic dimensions, and propose a family of test case prioritization techniques that selects pairs of test case without replacement in turn. To the best of their knowledge, it is the first test case prioritization proposal that selects test case pairs for prioritization. The authors validate their techniques by a suite of benchmarks. The empirical results show that when incorporating all dimensions, some members of our technique family can be more effective than conventional coverage-based techniques.
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8

Dahiya, Omdev, and Kamna Solanki. "An Efficient APHT Technique for Requirement-Based Test Case Prioritization." International Journal of Engineering Trends and Technology 69, no. 4 (April 25, 2021): 215–27. http://dx.doi.org/10.14445/22315381/ijett-v69i4p230.

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9

Tiutin, C. M., M. T. Trifan, and A. Vescan. "Defect Prediction-Based Test Case Prioritization." Studia Universitatis Babeș-Bolyai Informatica 65, no. 2 (December 14, 2020): 78. http://dx.doi.org/10.24193/subbi.2020.2.06.

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Changes in the software necessitate confirmation testing and regression testing to be applied since new errors may be introduced with the modification. Test case prioritization is one method that could be applied to optimize which test cases should be executed first, involving how to schedule them in a certain order that detect faults as soon as possible.The main aim of our paper is to propose a test case prioritization technique by considering defect prediction as a criteria for prioritization in addition to the standard approach which considers the number of discovered faults. We have performed several experiments, considering only faults and the defect prediction values for each class. We compare our approach with random test case execution (for a theoretical example) and with the fault-based approach (for the Mockito project). The results are encouraging, for several class changes we obtained better results with our proposed hybrid approach.
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Gupta, Varun, D. S. Chauhan, and Kamlesh Dutta. "Requirement Prioritization of Complex Web 2.0 Application based on Effects on Regression Testing." International Journal of Systems and Service-Oriented Engineering 5, no. 3 (July 2015): 18–37. http://dx.doi.org/10.4018/ijssoe.2015070102.

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Web 2.0 applications are complex information systems. Likewise any desktop applications, web applications are complex and require effective requirement prioritization preceded by effective decision aspect prioritization by involving diverse stakeholders. During the release of new increments, objective is to provide value to the software and simultaneously lowering the regression testing effort. This can be achieved by implementing all highest priority requirements along with those dependent on them so that next increments implemented requirements independent of already implemented ones. The challenges involved in aspect selection, requirement prioritization, and effective selection of the security requirements of Web 2.0 application makes its incremental deliveries a complex task as compared to that of desktop applications. The proposed requirement prioritization process is hybrid approach i.e. Based on combination of negotiations and methods to prioritize both decision aspects and software requirements. This technique reduces regression testing effort by taking an impact on regression testing as one of the parameters during prioritization and overcomes various problems related to prioritization of web 2.0 applications. This technique is applied on live system of “Virtual Classroom”, by employing three stakeholder groups with total 8 stakeholders. Results were promising since it resulted in the successful delivery of web application due to effective aspect and requirement prioritization thereby leading to reduced regression testing effort.
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11

Alrawashdeh, Thamer, Fuad ElQirem, Ahmad Althunibat, and Roba Alsoub. "A Prioritization Approach for Regression Test Cases Based on a Revised Genetic Algorithm." Information Technology and Control 50, no. 3 (September 24, 2021): 443–57. http://dx.doi.org/10.5755/j01.itc.50.3.27662.

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The regression testing is a software-based testing approach executed to verify that changes made to the softwaredo not affect the existing functionality of the product. On account of the constraints of time and cost, it isimpractical to re-execute all the test cases for software whenever a change occurs. In order to overcome sucha problem in the selection of regression test cases, a prioritization technique should be employed. On the basisof some predefined criterion, the prioritization techniques create an execution schedule for the test cases, sothe higher priority test cases can be performed earlier than the lower priority test cases in order to improvethe efficiency of the software testing. Many prioritization criteria for regression test cases have been proposedin software testing literature; however, most of such techniques are code-based. Keeping in view this fact, thisresearch work has proposed a prioritization approach for regression test cases generated from software specificationswhich are based on the criterion of the Average Percentage Transition Coverage (APTC) by using arevised genetic algorithm. This criterion evaluates the rate of transitions coverage by incorporating knowledgeabout the significance of transitions between activates in the form of weights. APTC has been used as a fitnessevaluation function in a genetic algorithm to measure the effectiveness of a test cases sequence. Moreover, inorder to improve the coverage percentage, the proposed approach has revised the genetic algorithm by solvingthe problem of the optimal local solution. The experimental results show that the proposed approach demonstratesa good coverage performance with less execution time as compared to the standard genetic algorithmand some other prioritization techniques.
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Wang, Rongcun, Zhengmin Li, Shujuan Jiang, and Chuanqi Tao. "Regression Test Case Prioritization Based on Fixed Size Candidate Set ART Algorithm." International Journal of Software Engineering and Knowledge Engineering 30, no. 03 (March 2020): 291–320. http://dx.doi.org/10.1142/s0218194020500138.

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Regression testing is a very time-consuming and expensive testing activity. Many test case prioritization techniques have been proposed to speed up regression testing. Previous studies show that no one technique is always best. Random strategy, as the simplest strategy, is not always so bad. Particularly, when a test suite has higher fault detection capability, the strategy can generate a better result. Nevertheless, due to the randomness, the strategy is not always as satisfactory as expected. In this context, we present a test case prioritization approach using fixed size candidate set adaptive random testing algorithm to reduce the effect of randomness and improve fault detection effectiveness. The distance between pair-wise test cases is assessed by exclusive OR. We designed and conducted empirical studies on eight C programs to validate the effectiveness of the proposed approach. The experimental results, confirmed by a statistical analysis, indicate that the approach we proposed is more effective than random and the total greedy prioritization techniques in terms of fault detection effectiveness. Although the presented approach has comparable fault detection effectiveness to ART-based and the additional greedy techniques, the time cost is much lower. Consequently, the proposed approach is much more cost-effective.
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Mohamed, Noha, Sherin Moussa, Nagwa Badr, and Mohamed Tolba. "Enhancing Test Cases Prioritization for Internet of Things based systems using Search-based Technique." International Journal of Intelligent Computing and Information Sciences 21, no. 1 (February 1, 2021): 84–94. http://dx.doi.org/10.21608/ijicis.2021.69462.1076.

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Durán and Durán. "Prioritization of Physical Assets for Maintenance and Production Sustainability." Sustainability 11, no. 16 (August 8, 2019): 4296. http://dx.doi.org/10.3390/su11164296.

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Maintenance is one of the main drivers for ensuring the sustainability of manufacturing in capital-intensive production systems. However, in many cases, as a result of budgetary constraints, it is necessary to select which machines will be subject to maintenance actions on the basis of a list of priorities. Traditionally, such prioritization decisions are made using techniques which, in general, do not consider the systemic impact of maintenance actions. This paper is focused at the definition of a novel graphical technique for physical assets prioritization. This technique is aimed at establishing maintenance priorities considering the impact on the overall throughput of the production system. Due to the fact that the proposed technique is based on the well-known Overall Equipment Effectiveness (OEE) index, its use is not only limited to measuring the effects of maintenance decisions and actions (availability), but also to operational implications (productivity and utilization). The results could be displayed graphically, which makes it particularly useful and manageable for different types of users. The methodology is illustrated though an example based on a comminution plant from the copper mine industry. It is evident how the proposed technique facilitates business-oriented and economic sustainability focused decisions.
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Huang, Yu-Chi, Kuan-Li Peng, and Chin-Yu Huang. "A history-based cost-cognizant test case prioritization technique in regression testing." Journal of Systems and Software 85, no. 3 (March 2012): 626–37. http://dx.doi.org/10.1016/j.jss.2011.09.063.

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Khan, Faisal I., and Rehan Sadiq. "Risk-Based Prioritization of Air Pollution Monitoring Using Fuzzy Synthetic Evaluation Technique." Environmental Monitoring and Assessment 105, no. 1-3 (June 2005): 261–83. http://dx.doi.org/10.1007/s10661-005-3852-1.

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Singh, Dileep Kumar, and Praveen Kaushik. "Intrusion response prioritization based on fuzzy ELECTRE multiple criteria decision making technique." Journal of Information Security and Applications 48 (October 2019): 102359. http://dx.doi.org/10.1016/j.jisa.2019.102359.

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18

Kandil, Passant, Sherin Moussa, and Nagwa Badr. "Cluster-based test cases prioritization and selection technique for agile regression testing." Journal of Software: Evolution and Process 29, no. 6 (July 22, 2016): e1794. http://dx.doi.org/10.1002/smr.1794.

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Ahmed, Farrukh Shahzad, Awais Majeed, Tamim Ahmed Khan, and Shahid Nazir Bhatti. "Value-based cost-cognizant test case prioritization for regression testing." PLOS ONE 17, no. 5 (May 17, 2022): e0264972. http://dx.doi.org/10.1371/journal.pone.0264972.

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Software Test Case Prioritization (TCP) is an effective approach for regression testing to tackle time and budget constraints. The major benefit of TCP is to save time through the prioritization of important test cases first. Existing TCP techniques can be categorized as value-neutral and value-based approaches. In a value-based fashion, the cost of test cases and severity of faults are considered whereas, in a value-neutral fashion these are not considered. The value-neutral fashion is dominant over value-based fashion, and it assumes that all test cases have equal cost and all software faults have equal severity. But this assumption rarely holds in practice. Therefore, value-neutral TCP techniques are prone to produce unsatisfactory results. To overcome this research gap, a paradigm shift is required from value-neutral to value-based TCP techniques. Currently, very limited work is done in a value-based fashion and to the best of the authors’ knowledge, no comprehensive review of value-based cost-cognizant TCP techniques is available in the literature. To address this problem, a systematic literature review (SLR) of value-based cost-cognizant TCP techniques is presented in this paper. The core objective of this study is to combine the overall knowledge related to value-based cost-cognizant TCP techniques and to highlight some open research problems of this domain. Initially, 165 papers were reviewed from the prominent research repositories. Among these 165 papers, 21 papers were selected by using defined inclusion/exclusion criteria and quality assessment procedures. The established questions are answered through a thorough analysis of the selected papers by comparing their research contributions in terms of the algorithm used, the performance evaluation metric, and the results validation method used. Total 12 papers used an algorithm for their technique but 9 papers didn’t use any algorithm. Particle Swarm Optimization (PSO) Algorithm is dominantly used. For results validation, 4 methods are used including, Empirical study, Experiment, Case study, and Industrial case study. The experiment method is dominantly used. Total 6 performance evaluation metrics are used and the APFDc metric is dominantly used. This SLR yields that value-orientation and cost cognition are vital in the TCP process to achieve its intended goals and there is great research potential in this research domain.
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Bello, Abdulkarim, Abubakar Md Sultan, Abdul Azim Abdul Ghani, and Hazura Zulzalil. "Evolutionary Cost Cognizant Regression Test Prioritization for Object-Oriented Programs Based on Fault Dependency." International Journal of Engineering & Technology 7, no. 4.1 (September 12, 2018): 28. http://dx.doi.org/10.14419/ijet.v7i4.1.19486.

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Regression testing performed to provide confidence on the newly or updated software system is a resource consuming process. To ease this process, various techniques are developed. One such technique, test case prioritization, orders test cases with respect to the goals such that the most important test case in achieving those goals is scheduled earlier during the testing session. Among such performance goals, the rate of faults detections, measure how faults are detected quickly throughout the regression testing process. Improved dependency detection among faults provides faster feedback to the developers which gives chance to debug leading faults earlier in time. One other goal, the rate of fault severity detection, measure how fast severe fault can be detected in the testing process. Although, previous works address these issues but assumed that the costs of executing test cases and severities of detected faults are the same. However, costs of test and severities of faults varied. Furthermore, they did not consider incorporating evolution process such as applying genetic algorithms to their technique. In this work, we proposed an evolutionary cost-cognizant regression testing approach that prioritizes test case according to the rate of severity detection of test cases connected with dependent faults using genetic algorithms. The aim is to reveal more severe leading faults earlier using least cost in executing the test suite and to measure the efficacy of the technique using APFDc.
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Xiao, Lei, Huaikou Miao, and Ying Zhong. "Test case prioritization and selection technique in continuous integration development environments: a case study." International Journal of Engineering & Technology 7, no. 2.28 (May 16, 2018): 332. http://dx.doi.org/10.14419/ijet.v7i2.28.13207.

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Regression testing is a very important activity in continuous integration development environments. Software engineers frequently integrate new or changed code that involves in a new regression testing. Furthermore, regression testing in continuous integration development environments is together with tight time constraints. It is also impossible to re-run all the test cases in regression testing. Test case prioritization and selection technique are often used to render continuous integration processes more cost-effective. According to multi objective optimization, we present a test case prioritization and selection technique, TCPSCI, to satisfy time constraints and achieve testing goals in continuous integration development environments. Based on historical failure data, testing coverage code size and testing execution time, we order and select test cases. The test cases of the maximize code coverage, the shorter execution time and revealing the latest faults have the higher priority in the same change request. The case study results show that using TCPSCI has a higher cost-effectiveness comparing to the manually prioritization.
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RAM, SEWAK. "Watershed Prioritization based on LULC Characteristics using GIS and TOPSIS: A case of Chathe watershed." Annals of Plant and Soil Research 24, no. 3 (August 1, 2022): 505–11. http://dx.doi.org/10.47815/apsr.2021.10201.

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Watershed prioritization based on land use and land cover demonstrated the role of different features on possibilities of soil erosion. Sub-watershed prioritization is more crucial to restrict the soil erosion as well as loss of nutrients from the top soil. The study assigned the weight to various LULC features based on their role in soil erosion. Finally with the help of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), LULC-based prioritization index has been constructed and prioritized the seven sub-watersheds.Highest acreage of agricultural fallow land and barren scrubland with less amount of forest coverage in WS5 turns into more susceptible category compared to others, whereas lower acreage of barren scrubland, agricultural fallow land and cropland with higher coverage of forest in WS6 turns into lower susceptible category.
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Dahiya, Omdev, and Kamna Solanki. "An Efficient Requirement-based Test Case Prioritization Technique using Optimized TFC-SVM Approach." International Journal of Engineering Trends and Technology 69, no. 1 (January 25, 2021): 5–16. http://dx.doi.org/10.14445/22315381/ijett-v69i1p202.

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SURI, BHARTI, and SHWETA SINGHAL. "DEVELOPMENT AND VALIDATION OF AN IMPROVED TEST SELECTION AND PRIORITIZATION ALGORITHM BASED ON ACO." International Journal of Reliability, Quality and Safety Engineering 21, no. 06 (December 2014): 1450032. http://dx.doi.org/10.1142/s0218539314500326.

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Regression testing is an important and often costly software maintenance activity. Retesting the software using existing test suite whenever modifications are made to the system, in order to regain confidence in correctness of the system, is called as Regression Testing. Regression test suites are often too large to re-execute in the given time and cost constraints. Reordering of the test suite is done according to appropriate criteria like code, branch, condition and fault coverage, etc. This process is known as Test Suite Prioritization. We can also select a subset of the original test suite on the basis of some criteria, often called as Regression Test Selection. The research problem that arises from this is the choice of technique or process to be used for selecting and prioritizing according to one or more of the chosen criteria(s). Ant Colony Optimization (ACO) is one such technique that was used by Singh et al. for solving Time-Constrained Test Suite Selection and Prioritization problem using Fault Exposing Potential (FEP). In this paper, we propose improvements to the existing algorithm along with details of the time complexity of the algorithm. It was very convincing to implement the technique considering the results obtained. Implementation of the proposed algorithm has also been demonstrated. The tool was repeatedly run on sample programs by changing the time constraint criterion. The analysis shows the usefulness and effectiveness of using ACO technique for test suite selection and prioritization.
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Wang, Xiaolin, Hongwei Zeng, Honghao Gao, Huaikou Miao, and Weiwei Lin. "Location-Based Test Case Prioritization for Software Embedded in Mobile Devices Using the Law of Gravitation." Mobile Information Systems 2019 (January 2, 2019): 1–14. http://dx.doi.org/10.1155/2019/9083956.

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Considering that some intelligent software in mobile devices is related to location of sensors and devices, regression testing for it faces a major challenge. Test case prioritization (TCP), as a kind of regression test optimization technique, is beneficial to improve test efficiency. However, traditional TCP techniques may have limitations on testing intelligent software embedded in mobile devices because they do not take into account characteristics of mobile devices. This paper uses a smart mall as a scenario to design a novel location-based TCP technique for software embedded in mobile devices using the law of gravitation. First, test gravitation is proposed by applying the idea of universal gravitation. Second, a specific calculation model of test gravitation is designed for a smart mall scenario. Third, how to create a faulted test case set is designed by the pseudocode. Fourth, a location-based TCP using the law of gravitation algorithm is proposed, which utilizes test case information, fault information, and location information to prioritize test cases. Finally, an empirical evaluation is presented by using one industrial project. The observation, underlying the experimental results, is that our proposed TCP approach performs better than traditional TCP techniques. In addition, besides location information, the level of devices is also an important factor which affects the prioritization efficiency.
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Polisena, Julie, Leonor Varela-Lema, Iñaki Gutiérrez-Ibarluzea, and Brian Godman. "VP01 A Disinvestment Toolkit: The Prioritization Of Technologies Of No Or Low Added Value." International Journal of Technology Assessment in Health Care 34, S1 (2018): 159. http://dx.doi.org/10.1017/s0266462318003355.

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Introduction:Candidate health technologies identified for disinvestment will require prioritization depending on the system's capacity for dealing with the assessments or for further considerations. Compilations of low value lists, such as the National Institutes for Health and Clinical Excellence's, “Do not do recommendations”, can serve as databases for prioritization topics. Prioritization processes can also be triggered by experience or event-based regional requests and decisions; new evidence on safety, effectiveness and cost-effectiveness, variations in clinical practice, patient or consumer voicing, discrepancies between practice and guidelines; and or time-based mechanisms, such as approval of new health technologies and reassessment five years after introduction.Methods:A search of the published and grey literature was conducted to identify the current methods or tools used to prioritize potential health technologies and services for disinvestment. The description of the methods and tools identified, the prioritization criteria, and the stakeholders involved in the process were reviewed and summarized.Results:The methods and tools used for prioritization that were identified in the literature include the PriTec Prioritization tool, nominal group technique, Program Budgeting and Marginal Analysis, consensus building, and online surveys. Further, common criteria for prioritization centered on the disease burden, possible risks and benefits, costs and cost-effectiveness, utilization, and time-based criteria. Prioritization can be conducted by health care professionals, decision makers, patients or patient groups and representative community members.Conclusions:The prioritization process for disinvestment candidates should be transparent and guided largely by evidence. It is highly recommended that the list of predefined criteria be developed with input from all relevant stakeholders to meet the objectives of the specific health care setting. The commonly cited basic requirements include clinical parameters, economic measures, and social, ethical or legal considerations.
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HUANG, RUBING, XIAODONG XIE, DAVE TOWEY, TSONG YUEH CHEN, YANSHENG LU, and JINFU CHEN. "PRIORITIZATION OF COMBINATORIAL TEST CASES BY INCREMENTAL INTERACTION COVERAGE." International Journal of Software Engineering and Knowledge Engineering 23, no. 10 (December 2013): 1427–57. http://dx.doi.org/10.1142/s0218194013500459.

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Combinatorial interaction testing is a well-recognized testing method, and has been widely applied in practice, often with the assumption that all test cases in a combinatorial test suite have the same fault detection capability. However, when testing resources are limited, an alternative assumption may be that some test cases are more likely to reveal failure, thus making the order of executing the test cases critical. To improve testing cost-effectiveness, prioritization of combinatorial test cases is employed. The most popular approach is based on interaction coverage, which prioritizes combinatorial test cases by repeatedly choosing an unexecuted test case that covers the largest number of uncovered parameter value combinations of a given strength (level of interaction among parameters). However, this approach suffers from some drawbacks. Based on previous observations that the majority of faults in practical systems can usually be triggered with parameter interactions of small strengths, we propose a new strategy of prioritizing combinatorial test cases by incrementally adjusting the strength values. Experimental results show that our method performs better than the random prioritization technique and the technique of prioritizing combinatorial test suites according to test case generation order, and has better performance than the interaction-coverage-based test prioritization technique in most cases.
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Palak, Palak, and Preeti Gulia. "Ant Colony Optimization Based Test Case Selection for Component Based Software." International Journal of Engineering & Technology 7, no. 4 (September 26, 2018): 2743. http://dx.doi.org/10.14419/ijet.v7i4.17565.

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Reusability is one of the prime aspects of high quality software. Based on the concept of reusing the previous effort, Component based software engineering is a widely evolving software development paradigm that sets new challenges for testing team. The third party components need to be selected and assembled in development framework. Components interact with each other for various services and the interface between them can prove as the point of failure. As exhaustive testing of all interaction sequences is not possible, there is need for automated test case reduction and prioritization techniques to increase the efficiency of testing process. Ant Colony Optimization (ACO), a nature inspired optimization technique has wide range of applications in the field of software engineering. This paper presents an ACO based technique for test case selection for interaction testing of reusable software components.
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Gupta, Varun, D. S. Chauhan, and Kamlesh Dutta. "Regression Testing-Based Requirement Prioritization of Mobile Applications." International Journal of Systems and Service-Oriented Engineering 3, no. 4 (October 2012): 20–39. http://dx.doi.org/10.4018/jssoe.2012100102.

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Mobile software application development process must be matured enough to handle the challenges (especially market related) associated with the development of high quality mobile software development. Ever increasing number of both mobile users and mobile applications had presented software engineers with the challenge of satisfying billions of users with high quality software applications to be delivered within deadline and budgets. Always there had been a lot of pressure to develop complex software categorized by thousands of requirements, under resource constrained environment. Requirement prioritization is one of the activities undertaken by software engineer to deliver partial software product to its customers such that most important requirements are implemented in the earliest releases. During next releases some changed and pending requirements are implemented, an activity that generates ripple effects. Such ripple effects need to be tested by executing modified source code against test cases of previous releases (regression testing). Regression testing is a very effortful activity that requires a software tester to select test cases that have high fault detection capability, execute the modified code against selected test cases and performing debugging. This regression testing activity can be lowered to the maximum extend by considering dependencies between requirements during the time of requirement prioritization. Thus requirement prioritization will be carried out not only against aspects like cost, time, risks, business values etc but against dependencies also. The aim is to implement almost all dependent highest priority requirements in current release so that implementation of new requirements is unlikely to have ripple effects. Changes in requirements might not be related to variable usage and definition and might not involve a change in functionality. In such cases there is no need to select already executed test cases of previous versions. Module dependencies can lead to test case selections of previous versions if changes of requirement lead to ripple effects. This paper aims to implement highest priority requirements such that regression testing is performed to minimum thereby improving development process of mobile applications. The proposed technique had been successfully evaluated on Android based notification software application that meets the specification of Aakash tablets.
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Gupta, Rahul, and Akhilesh Kumar Yadav. "STUDY OF TEST CASE PRIORITIZATION TECHNIQUE USING APFD." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 3 (August 5, 2013): 1475–81. http://dx.doi.org/10.24297/ijct.v10i3.3277.

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Regression testing is used to ensure that bugs are fixed and new functionality introduce in a new version of a software that don't adversely affect the original functionality inherited from the previous version.Regression testing is one of the most complaining activities of software development and maintenance.Unluckily, It may have feeble resources to allow for the re-execution of all test cases during regression testing. In this situation the use of test case prioritization is profitable because the best appropriate test cases are executed first. In this paper we are proposing an algorithm to prioritize test cases based on rate of fault detection and impact of fault.The proposed algorithm recognises the exhausting fault at earlier stage of the testing process.We are using an Average Percentage of Faults Detected (APFD) metric to determine the effectiveness of the new test case arrangements.
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31

Gaikwad, Ravindra, and Vijay Bhagat. "Multi-criteria Prioritization for Sub-watersheds in Medium River Basin using AHP and Influence Approaches." Hydrospatial Analysis 2, no. 1 (December 27, 2018): 61–82. http://dx.doi.org/10.21523/10.21523/gcj3.18020105.

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Watershed is unique bio-physical unit of the Earth surface and source of resources to the human, animal and plants. AHP based multi-criteria analysis is suitable for prioritization of sub-watersheds in medium river basin for planning, management and development. Twenty five criterion i.e. area, morphimery, geology, slope, soil, rainfall and population density were selected for prioritization of medium watersheds of Upper Mula basin in Maharashtra (India). Correlation analysis is suitable for ranking the criterion selected for prioritization. Texture Ratio (25.94%), drainage texture (12.97%), stream order (8.65%), total stream length (6.49%) and ruggedness number (5.19%) show higher influences on development of watershed structure in the study area. Further, criterion like geology, rainfall, soil and population were show considerable influence in prioritization of sub-watersheds in medium river basin. Influences were estimated based on weights calculated using AHP technique. Values of influences were normalized using distribution of particular criterion within sub-watersheds. Watersheds are classified into high, moderate and low priorities. The methodology formulated in this study can be effective tool for quick prioritization of medium and major watersheds for planning and management for development.
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Gaikwad, Ravindra, and Vijay Bhagat. "Multi-criteria Prioritization for Sub-watersheds in Medium River Basin using AHP and Influence Approaches." Hydrospatial Analysis 2, no. 1 (December 27, 2018): 61–82. http://dx.doi.org/10.21523/gcj3.18020105.

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Watershed is unique bio-physical unit of the Earth surface and source of resources to the human, animal and plants. AHP based multi-criteria analysis is suitable for prioritization of sub-watersheds in medium river basin for planning, management and development. Twenty five criterion i.e. area, morphimery, geology, slope, soil, rainfall and population density were selected for prioritization of medium watersheds of Upper Mula basin in Maharashtra (India). Correlation analysis is suitable for ranking the criterion selected for prioritization. Texture Ratio (25.94%), drainage texture (12.97%), stream order (8.65%), total stream length (6.49%) and ruggedness number (5.19%) show higher influences on development of watershed structure in the study area. Further, criterion like geology, rainfall, soil and population were show considerable influence in prioritization of sub-watersheds in medium river basin. Influences were estimated based on weights calculated using AHP technique. Values of influences were normalized using distribution of particular criterion within sub-watersheds. Watersheds are classified into high, moderate and low priorities. The methodology formulated in this study can be effective tool for quick prioritization of medium and major watersheds for planning and management for development.
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Chaurasia, Geetanjali, and Sonali Agarwal. "A Hybrid Approach of Clustering and Time-Aware Based Novel Test Case Prioritization Technique." International Journal of Database Theory and Application 9, no. 4 (April 30, 2016): 23–44. http://dx.doi.org/10.14257/ijdta.2016.9.4.02.

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34

Alhubaishy, Abdulaziz, and Abdulmajeed Aljuhani. "A Load-Fairness Prioritization-Based Matching Technique for Cloud Task Scheduling and Resource Allocation." Computer Systems Science and Engineering 45, no. 3 (2023): 2461–81. http://dx.doi.org/10.32604/csse.2023.032217.

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35

Alsukhni, Emad, Ahmad A. Saifan, and Hanadi Alawneh. "A New Data Mining-Based Framework to Test Case Prioritization Using Software Defect Prediction." International Journal of Open Source Software and Processes 8, no. 1 (January 2017): 21–41. http://dx.doi.org/10.4018/ijossp.2017010102.

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Test cases do not have the same importance when used to detect faults in software; therefore, it is more efficient to test the system with the test cases that have the ability to detect the faults. This research proposes a new framework that combines data mining techniques to prioritize the test cases. It enhances fault prediction and detection using two different techniques: 1) the data mining regression classifier that depends on software metrics to predict defective modules, and 2) the k-means clustering technique that is used to select and prioritize test cases to identify the fault early. Our approach of test case prioritization yields good results in comparison with other studies. The authors used the Average Percentage of Faults Detection (APFD) metric to evaluate the proposed framework, which results in 19.9% for all system modules and 25.7% for defective ones. Our results give us an indication that it is effective to start the testing process with the most defective modules instead of testing all modules arbitrary arbitrarily.
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BERANDER, PATRIK, and PER JÖNSSON. "HIERARCHICAL CUMULATIVE VOTING (HCV) — PRIORITIZATION OF REQUIREMENTS IN HIERARCHIES." International Journal of Software Engineering and Knowledge Engineering 16, no. 06 (December 2006): 819–49. http://dx.doi.org/10.1142/s0218194006003026.

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Decision support in requirements engineering is an activity that plays an important role in enabling the delivery of value to stakeholders. Requirements prioritization has been identified as an integral (and important) part of requirements negotiation and release planning in incremental software development, which makes prioritization a key issue in requirements engineering decision support. The Analytical Hierarchy Process (AHP) has for long been considered as the technique to use when prioritizing requirements on a ratio scale. Several studies have reported positively about AHP, but lately a number of studies have also reported about weaknesses, without identifying any better ratio-scale alternatives. In this paper, the strengths and weaknesses of AHP and another ratio-scale prioritization technique, Cumulative Voting (CV), are compared. Based on this comparison, a new technique for prioritizing hierarchically structured requirements on a ratio scale is presented, called Hierarchical Cumulative Voting (HCV). HCV addresses the weaknesses of AHP while inheriting the strengths of CV. The suitability of HCV is discussed theoretically as well as in the light of empirical results from using HCV and CV in industrial settings. It is concluded that HCV seems very promising, but additional empirical studies are needed to address some of the open questions about the technique.
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37

Nunchhani, V., T. Golom, L. Nirin, A. Bandyopadhyay, and A. Bhadra. "Prioritization of Mago Basin based on erodibility through morphometric analysis using GIS technique: A PCA-based approach." Journal of Soil and Water Conservation 19, no. 1 (2020): 10. http://dx.doi.org/10.5958/2455-7145.2020.00002.8.

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38

Ali, Atif, Yaser Hafeez, Sadia Ali, Shariq Hussain, Shunkun Yang, Arif Jamal Malik, and Aaqif Afzaal Abbasi. "A Data Mining Technique to Improve Configuration Prioritization Framework for Component-Based Systems: An Empirical Study." Information Technology and Control 50, no. 3 (September 24, 2021): 424–42. http://dx.doi.org/10.5755/j01.itc.50.3.27622.

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Department of Software Engineering, In the current application development strategies, families of productsare developed with personalized configurations to increase stakeholders’ satisfaction. Product lines have theability to address several requirements due to their reusability and configuration properties. The structuringand prioritizing of configuration requirements facilitate the development processes, whereas it increases theconflicts and inadequacies. This increases human effort, reducing user satisfaction, and failing to accommodatea continuous evolution in configuration requirements. To address these challenges, we propose a framework formanaging the prioritization process considering heterogeneous stakeholders priority semantically. Featuresare analyzed, and mined configuration priority using the data mining method based on frequently accessed andchanged configurations. Firstly, priority is identified based on heterogeneous stakeholder’s perspectives usingthree factors functional, experiential, and expressive values. Secondly, the mined configuration is based on frequentlyaccessed or changed configuration frequency to identify the new priority for reducing failures or errorsamong configuration interaction. We evaluated the performance of the proposed framework with the help ofan experimental study and by comparing it with analytical hierarchical prioritization (AHP) and Clustering.The results indicate a significant increase (more than 90 percent) in the precision and the recall value of theproposed framework, for all selected cases.
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39

Sánchez-Lozano, J. M., F. J. Salmerón-Vera, and C. Ros-Casajús. "Prioritization of Cartagena Coastal Military Batteries to Transform Them into Scientific, Tourist and Cultural Places of Interest: A GIS-MCDM Approach." Sustainability 12, no. 23 (November 26, 2020): 9908. http://dx.doi.org/10.3390/su12239908.

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This study presents a combination of multi-criteria decision-making (MCDM) methodologies with geographic information systems (GIS) to carry out a prioritization of obsolete military coastal batteries with the aim of transforming them into touristic, scientific, and cultural places of interest. The study area is located in the Municipality of Cartagena, in Southeast Spain. Such a prioritization requires taking into account transport criteria (distance to roads or train stations), infrastructure criteria (distance to electrical grids or distance to water tanks), touristic or scientific criteria (distance to towns, beaches, archaeological sites, assets of cultural interest, etc.), and orography criteria (area, altitude, and slope of each battery). Therefore, this decision problem involves a set of alternatives (coastal military batteries) to be prioritized based on a group of criteria that should be considered. To tackle this, GIS software is used to provide the attribute table of alternatives and criteria (decision matrix), and the proposed decision problem is solved through a combination of MCDM methodologies based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Analytic Hierarchy Process (AHP) techniques. The AHP approach is applied to determine the weights of the criteria whilst the TOPSIS method provides a ranking of alternatives in order to obtain a prioritization.
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40

Chandniha, Surendra Kumar, and Mitthan Lal Kansal. "Prioritization of sub-watersheds based on morphometric analysis using geospatial technique in Piperiya watershed, India." Applied Water Science 7, no. 1 (November 2, 2014): 329–38. http://dx.doi.org/10.1007/s13201-014-0248-9.

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41

Sun, Xiaobing, Xin Peng, Hareton Leung, and Bin Li. "ComboRT: A New Approach for Generating Regression Test Cases for Evolving Programs." International Journal of Software Engineering and Knowledge Engineering 26, no. 06 (August 2016): 1001–26. http://dx.doi.org/10.1142/s0218194016500340.

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Regression testing is essential to ensure software quality during software evolution. Two widely-used regression testing techniques, test case selection and prioritization, are used to maximize the value of the continuously enlarging test suite. However, few works consider both these two techniques together, which decreases the usefulness of the independently studied techniques in practice. In the presence of changes during program evolution, regression testing is usually conducted by selecting the test cases that cover the impact results of the changes. It seldom considers the false-positives in the information covered. Hence, the effectiveness of such regression testing techniques is decreased. In this paper, we propose an approach, ComboRT, which combines test case selection and prioritization together to directly generate a ranked list of test cases. It is based on the impact results predicted by the change impact analysis (CIA) technique, FCA–CIA, which generates a ranked list of impacted methods. Test cases which cover these impacted methods are included in the new test suite. As each method predicted by FCA–CIA is assigned with an impact factor value corresponding to the probability of this method to be impacted, test cases are then ordered according to the impact factor values of the impacted methods. Empirical studies on four Java based software systems demonstrate that ComboRT can be effectively used for regression testing in object-oriented Java-based software systems during their evolution.
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42

Takami, Hikaru, and Shigeru Obayashi. "A comparator-based constraint handling technique for evolutionary algorithms." AIP Advances 12, no. 5 (May 1, 2022): 055229. http://dx.doi.org/10.1063/5.0090572.

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Constraint handling is a key task for the successful optimization of design parameters in industrial design problems. This paper proposes a comparator-based constraint handling technique, called the More Less-Violations Method (MLVM), for solving real constrained optimization problems using evolutionary algorithms. The structure of the MLVM is simple and it can easily be integrated into conventional evolutionary algorithms. In the proposed method, constraint weights represent the level of importance of each constraint, enabling evolutionary compliance prioritization. Moreover, an acceptable region formed by the constraint tolerances allows trade-offs between objectives and constraints while preserving diverse solutions and improving optimization performance. These elements enable the appropriate design of industrial optimization problems. An application of this method to problems without constraint tolerances is also proposed. The JAXA/Mazda benchmark problem, developed on a real-world constrained design optimization dataset, is used to assess the performance of the MLVM. The results indicate that the MLVM realizes encouraging optimization performance.
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43

Xia, Jingbo, Xing Zhang, Daojun Yuan, Lingling Chen, Jonathan Webster, and Alex Chengyu Fang. "Gene Prioritization of Resistant Rice Gene againstXanthomas oryzae pv. oryzaeby Using Text Mining Technologies." BioMed Research International 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/853043.

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To effectively assess the possibility of the unknown rice protein resistant toXanthomonas oryzae pv. oryzae, a hybrid strategy is proposed to enhance gene prioritization by combining text mining technologies with a sequence-based approach. The text mining technique of term frequency inverse document frequency is used to measure the importance of distinguished terms which reflect biomedical activity in rice before candidate genes are screened and vital terms are produced. Afterwards, a built-in classifier under the chaos games representation algorithm is used to sieve the best possible candidate gene. Our experiment results show that the combination of these two methods achieves enhanced gene prioritization.
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44

Tahir, Hassan, Saif Ur Rehman Khan, and Syed Sohaib Ali. "LCBPA: An Enhanced Deep Neural Network-Oriented Bug Prioritization and Assignment Technique Using Content-Based Filtering." IEEE Access 9 (2021): 92798–814. http://dx.doi.org/10.1109/access.2021.3093170.

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45

Kirkire, Milind Shrikant, Santosh B. Rane, and Gayatri Jayant Abhyankar. "Structural equation modelling – FTOPSIS approach for modelling barriers to product development in medical device manufacturing industries." Journal of Modelling in Management 15, no. 3 (January 13, 2020): 967–93. http://dx.doi.org/10.1108/jm2-09-2018-0139.

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Purpose The purpose of this paper model and prioritizes barriers to product development in medical device manufacturing industries using an integrated “structural equation modelling” (SEM) and “fuzzy technique for order performance by similarity to ideal solution” (FTOPSIS) framework. Design/methodology/approach Barriers to medical device development (MDD) are adopted from literature. The initial structural model is proposed, exploratory factor analysis and confirmatory factor analysis are used to determine factor loading and model fit, respectively. Further, FTOPSIS is used to rank the barriers and sensitivity analysis is carried to check the robustness of results. The results are discussed in detail and the recommendations to overcome the barriers are presented. Findings Barriers analysed and prioritized in this research significantly hinder the MDD. The expert survey is used to develop an initial structural equation model of barriers to MDD, find the reliability and validity of the model. Based on the opinion of the experts, the barriers are divided into three categories – internal, policy and induced barriers. FTOPSIS is applied to rank and prioritize the barriers based on views from these three classes of experts. More reliance on imported devices leading to increased imports (B11) and lack of uniform regulatory standards (B6) are found to have the highest rank together, indicating these to be the most important barriers from the perspective considered here. Sensitivity analysis indicates that the factors are less sensitive to the weights of criteria further confirming the reliability of the initial solution. Research limitations/implications The prioritization of barriers may vary based upon experts. Policymakers, existing and new device developers need to give utmost importance to these barriers, which will help to accelerate the indigenous development of medical devices to overcome the present dependence on imports. Practical implications This paper demonstrates an integrated structural based modelling and prioritization technique for statistical modelling and prioritization of barriers to MDD. The results and recommendations will help policymakers and manufacturers to increase the indigenous share of medical devices. The integrated methodology can be effectively applied where the need for the combined quantitative and qualitative approach is there. Originality/value This paper demonstrates an effective structural based modelling and prioritization technique. It can be effectively applied in various fields, it will help policymakers and manufacturers to increase the indigenous share of medical devices.
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46

Choochotkaew, Sunyanan, Hirozumi Yamaguchi, and Teruo Higashino. "Two-Tier VoI Prioritization System on Requirement-Based Data Streaming toward IoT." Mobile Information Systems 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/7892545.

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Toward the world of Internet of Things, people utilize knowledge from sensor streams in various kinds of smart applications. The number of sensing devices is rapidly increasing along with the amount of sensing data. Consequently, a bottleneck problem at the local gateway has attracted high concern. An example scenario is smart elderly houses in rural areas where each house installs thousands of sensors and all connect to resource-limited and unstable 2G/3G networks. The bottleneck state can incur unacceptable latency and loss of significant data due to the limited waiting-queue. Orthogonally to the existing solutions, we propose a two-tier prioritization system to enhance information quality, indicated by VoI, at the local gateway. The proposed system has been designed to support several requirements with several conflicting criteria over shared sensing streams. Our approach adopts Multicriteria Decision Analysis technique to merge requirements and to assess the VoI. We introduce the framework that can reduce the computational cost by precalculation. Through a case study of building management systems, we have shown that our merge algorithm can provide 0.995 cosine-similarity for representing all user requirements and the evaluation approach can obtain satisfaction values around 3 times higher than the naïve strategies for the top-list data.
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47

Gouda, Ram, and V. Chandraprakash. "Multi-Objective Crow Search and Fruit Fly Optimization for Combinatorial Test Case Prioritization." International Journal of Software Innovation 9, no. 4 (October 2021): 1–19. http://dx.doi.org/10.4018/ijsi.289173.

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This paper proposes a novel test case prioritization technique, namely Multi- Objective Crow Search and Fruitfly Optimization (MOCSFO) for test case prioritization. The proposed MOCSFO is designed by integrating Crow search algorithm (CSA) and Chaotic Fruitfly optimization algorithm (CFOA). The optimal test cases are selected based on newly modelled fitness function, which consist of two parameters, namely average percentage of combinatorial coverage (APCC) and Normalized average of the percentage of faults detected (NAPFD). The test case to be selected is decided using a searching criterion or fitness based on sequential weighed coverage size. Accordingly, the effective searching criterion is formulated to determine the optimal test cases based on the constraints. The experimentation of the proposed MOCSFO method is performed by considering the performance metrics, like NAPFD, and APCC. The proposed MOCSFO outperformed the existing methods with enhanced NAPFD of 0.7, and APCC of 0.837.
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48

MARY, S. A. SAHAAYA ARUL, and R. KRISHNAMOORTHI. "TIME-AWARE AND WEIGHTED FAULT SEVERITY BASED METRICS FOR TEST CASE PRIORITIZATION." International Journal of Software Engineering and Knowledge Engineering 21, no. 01 (February 2011): 129–42. http://dx.doi.org/10.1142/s0218194011005116.

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Test metrics succeed in analyzing the current level of maturity in testing and give a projection on the way to proceed with testing activities by allowing us to set goals and predict future trends. The objective of test metrics is to capture the planned and actual quantities: the effort, time and resources required to complete all the phases of development of the software project. Test case prioritization is an effective and practical technique in regression testing. It schedules test cases in order of precedence that increases their ability to meet some performance goals, such as code coverage, rate of fault detection. In this paper, we present a new metrics, based on varying requirement priorities, test case priorities, test case execution time, and fault severities. The case study illustrates that the rate of "units-of-test-case-priority-satisfied- per-unit-test-case-time" can be increased, and with improvement on testing quality and customer satisfaction. To assess the practicality of our approach, we apply it to a realistic example from the industrial projects. Also we summarize a test process measurement project of TECHZONE™ (Software development Concern with Testing Department) test teams, and analyze the effectiveness of set of metrics for cost, time, and quality to measure the quality of test process based on the results of the proposed metrics.
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& Karim, Mohammed. "WATERSHED PRIORITIZATION ACROSS ERBIL PROVINCE FOR SOIL EROSION MANAGEMENT VIA MORPHOMETRIC ANALYSIS." IRAQI JOURNAL OF AGRICULTURAL SCIENCES 51, no. 5 (October 30, 2020): 1262–75. http://dx.doi.org/10.36103/ijas.v51i5.1134.

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Soil erosion is one of the foremost factors giving rise to watershed deterioration due to improper and unwise utilization of natural resources without proper vision, particularly in developing countries like Iraq. Since it is not possible to implement rehabilitation programs over all areas at a time, prioritization plays a major role in identification of the areas which are in need of immediate actions. Accordingly, the current study was conducted to perform morphometric analysis as the basis for prioritization. To achieve the above objective, 30 watersheds of different scales were delineated within Erbil governorate and standard procedures were followed to carry out morphometric analysis. Prioritization ranks were determined for the study watersheds based on computation of compound factors and on the technique for order preference by similarity to ideal solution (TOPSIS). The results indicated that nearly two approaches offered similar results. The regression analysis indicated that the priority rank from (TOPSIS) can be predicted from the priority rank from compound factor computation with a reasonable accuracy. Based on TOPSIS approach, watersheds: Kawlan-smelan, Nawandee, Warte, Prdi-qasre, Nawprdan, Darbandy-rayat, Dargalla and Mergasor fall within the very high priority class and as a consequence immediate actions should be taken to protect these watersheds. By contrast, the watersheds: Kasnazan, Smaquly, Bestana, Kawanyan, Rulka and Degala 1 are categorized under the low prioritization level. Further improvements in specifying the priority ranks can be expected upon coupling land use/land cover with morphometric analysis.
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Alrifaey, Moath, Tang Sai Hong, Eris Supeni, Azizan As’arry, and Chun Ang. "Identification and Prioritization of Risk Factors in an Electrical Generator Based on the Hybrid FMEA Framework." Energies 12, no. 4 (February 17, 2019): 649. http://dx.doi.org/10.3390/en12040649.

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The oil and gas industry is looking for ways to accurately identify and prioritize the failure modes (FMs) of the equipment. Failure mode and effect analysis (FMEA) is the most important tool used in the maintenance approach for the prevention of malfunctioning of the equipment. Current developments in the FMEA technique are mainly focused on addressing the drawbacks of the conventional risk priority number calculations, but the group effects and interrelationships of FMs on other measurements are neglected. In the present study, a hybrid distribution risk assessment framework was proposed to fill these gaps based on the combination of modified linguistic FMEA (LFMEA), Analytic Network Process (ANP), and Decision Making Trial and Evaluation Laboratory (DEMATEL) techniques. The hybrid framework of FMEA was conducted in a hazardous environment at a power generation unit in an oil and gas plant located in Yemen. The results show that mechanical and gas leakage FM in electrical generators posed a greater risk, which critically affects other FMs within the plant. It was observed that the suggested framework produced a precise ranking of FMs, with a clear relationship among FMs. Also, the comparisons of the proposed framework with previous studies demonstrated the multidisciplinary applications of the present framework.
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