Journal articles: 'Software defects'

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Malhotra, Ruchika, and Juhi Jain. "Predicting Software Defects for Object-Oriented Software Using Search-based Techniques." International Journal of Software Engineering and Knowledge Engineering 31, no. 02 (February 2021): 193–215. http://dx.doi.org/10.1142/s0218194021500054.

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Development without any defect is unsubstantial. Timely detection of software defects favors the proper resource utilization saving time, effort and money. With the increasing size and complexity of software, demand for accurate and efficient prediction models is increasing. Recently, search-based techniques (SBTs) have fascinated many researchers for Software Defect Prediction (SDP). The goal of this study is to conduct an empirical evaluation to assess the applicability of SBTs for predicting software defects in object-oriented (OO) softwares. In this study, 16 SBTs are exploited to build defect prediction models for 13 OO software projects. Stable performance measures — GMean, Balance and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) are employed to probe into the predictive capability of developed models, taking into consideration the imbalanced nature of software datasets. Proper measures are taken to handle the stochastic behavior of SBTs. The significance of results is statistically validated using the Friedman test complied with Wilcoxon post hoc analysis. The results confirm that software defects can be detected in the early phases of software development with help of SBTs. This paper identifies the effective subset of SBTs that will aid software practitioners to timely detect the probable software defects, therefore, saving resources and bringing up good quality softwares. Eight SBTs — sUpervised Classification System (UCS), Bioinformatics-oriented hierarchical evolutionary learning (BIOHEL), CHC, Genetic Algorithm-based Classifier System with Adaptive Discretization Intervals (GA_ADI), Genetic Algorithm-based Classifier System with Intervalar Rule (GA_INT), Memetic Pittsburgh Learning Classifier System (MPLCS), Population-Based Incremental Learning (PBIL) and Steady-State Genetic Algorithm for Instance Selection (SGA) are found to be statistically good defect predictors.

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Kumaresh, Sakthi, and Ramachandran Baskaran. "Mining Software Repositories for Defect Categorization." Journal of Communications Software and Systems 11, no. 1 (March 23, 2015): 31. http://dx.doi.org/10.24138/jcomss.v11i1.115.

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Early detection of software defects is very important to decrease the software cost and subsequently increase the software quality. Success of software industries not only depends on gaining knowledge about software defects, but largely reflects from the manner in which information about defect is collected and used. In software industries, individuals at different levels from customers to engineers apply diverse mechanisms to detect the allocation of defects to a particular class. Categorizing bugs based on their characteristics helps the Software Development team take appropriate actions to reduce similar defects that might get reported in future releases. Classification, if performed manually, will consume more time and effort. Human resource having expert testing skills & domain knowledge will be required for labeling the data. Therefore, the need of automatic classification of software defect is high.This work attempts to categorize defects by proposing an algorithm called Software Defect CLustering (SDCL). It aims at mining the existing online bug repositories like Eclipse, Bugzilla and JIRA for analyzing the defect description and its categorization. The proposed algorithm is designed by using text clustering and works with three major modules to find out the class to which the defect should be assigned. Software bug repositories hold software defect data with attributes like defect description, status, defect open and close date. Defect extraction module extracts the defect description from various bug repositories and converts it into unified format for further processing. Unnecessary and irrelevant texts are removed from defect data using data preprocessing module. Finally grouping of defect data into clusters of similar defect is done using clustering technique. The algorithm provides classification accuracy more than 80% in all of the three above mentioned repositories.

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Zhang, Wei, Zhen Yu Ma, Wen Ge Zhang, Qing Ling Lu, and Xiao Bing Nie. "Correlation Analysis of Software Defects Density and Metrics." Applied Mechanics and Materials 713-715 (January 2015): 2225–28. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2225.

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It is very useful for improving software quality if we can find which software metrics are more correlative with software defects or defects density. Based on 33 actual software projects, we analyzed 44 software metrics from application level, file level, class level and function level, and do correlation analysis with the number of software defects and defect density, the results show that software metrics have little correlation with the number of software defects, but are correlative with defect density. Through correlation analysis, we selected five metrics that have larger correlation with defect density, these metrics can be used for improving software quality and predicting software defects density.

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Henderson, Craig. "Managing software defects." ACM SIGSOFT Software Engineering Notes 33, no. 4 (July 2008): 1–3. http://dx.doi.org/10.1145/1384139.1384141.

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Han, Wan Jiang, He Yang Jiang, Yi Sun, and Tian Bo Lu. "Software Defect Distribution Prediction for BOSS System." Applied Mechanics and Materials 701-702 (December 2014): 67–70. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.67.

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Effective detection of software defects is an important activity of software development process. In this paper, we propose an approach to predict residual defects for BOSS project, which applies defect distribution model. Experiment results show that this approach can effectively improve the accuracy of defect prediction.

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Huh, Sang Moo, and Woo-Je Kim. "The Derivation of Defect Priorities and Core Defects through Impact Relationship Analysis between Embedded Software Defects." Applied Sciences 10, no. 19 (October 4, 2020): 6946. http://dx.doi.org/10.3390/app10196946.

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As embedded software is closely related to hardware equipment, any defect in embedded software can lead to major accidents. Thus, all defects must be collected, classified, and tested based on their severity. In the pure software field, a method of deriving core defects already exists, enabling the collection and classification of all possible defects. However, in the embedded software field, studies that have collected and categorized relevant defects into an integrated perspective are scarce, and none of them have identified core defects. Therefore, the present study collected embedded software defects worldwide and identified 12 types of embedded software defect classifications through iterative consensus processes with embedded software experts. The impact relation map of the defects was drawn using the decision-making trial and evaluation laboratory (DEMATEL) method, which analyzes the influence relationship between elements. As a result of analyzing the impact relation map, the following core embedded software defects were derived: hardware interrupt, external interface, timing error, device error, and task management. All defects can be tested using this defect classification. Moreover, knowing the correct test order of all defects can eliminate critical defects and improve the reliability of embedded systems.

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Kumaresh, Sakthi, and R. Baskaran. "Software Defect Prevention through Orthogonal Defect Classification (ODC)." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 3 (October 15, 2013): 2393–400. http://dx.doi.org/10.24297/ijct.v11i3.1166.

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â€œQuality is never an accident; it is always the result of intelligent effortâ€ [10]. In the process of making quality software product, it is necessary to have effective defect prevention process, which will minimize the risk of making defects /errors in software deliverables. An ideal approach would involve effective software development process with an integrated defect prevention process. This paper presents a Defect Prevention Model in which Defect Prevention Process(DPP) is integrated into software development life cycle to reduce the defects at early stages itself, thereby reducing the defect arrival rate as the project progresses to the subsequent stages. Orthogonal Defect Classification (ODC) scheme involving defect trigger, defect type etc. are discussed in this work to illustrate how ODC can be used in the defect prevention process. ODC can be used to measure development progress with respect to product quality and identify process problems, which will help to come out with â€œBest Practicesâ€ to be followed to eradicate the defects in the subsequent projects.

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Park, Jihyun, and Byoungju Choi. "Automatic Method for Distinguishing Hardware and Software Faults Based on Software Execution Data and Hardware Performance Counters." Electronics 9, no. 11 (November 2, 2020): 1815. http://dx.doi.org/10.3390/electronics9111815.

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Debugging in an embedded system where hardware and software are tightly coupled and have restricted resources is far from trivial. When hardware defects appear as if they were software defects, determining the real source becomes challenging. In this study, we propose an automated method of distinguishing whether a defect originates from the hardware or software at the stage of integration testing of hardware and software. Our method overcomes the limitations of the embedded environment, minimizes the effects on runtime, and identifies defects by obtaining and analyzing software execution data and hardware performance counters. We analyze the effects of the proposed method through an empirical study. The experimental results reveal that our method can effectively distinguish defects.

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Falessi, Davide, Aalok Ahluwalia, and Massimiliano DI Penta. "The Impact of Dormant Defects on Defect Prediction: A Study of 19 Apache Projects." ACM Transactions on Software Engineering and Methodology 31, no. 1 (January 31, 2022): 1–26. http://dx.doi.org/10.1145/3467895.

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Defect prediction models can be beneficial to prioritize testing, analysis, or code review activities, and has been the subject of a substantial effort in academia, and some applications in industrial contexts. A necessary precondition when creating a defect prediction model is the availability of defect data from the history of projects. If this data is noisy, the resulting defect prediction model could result to be unreliable. One of the causes of noise for defect datasets is the presence of “dormant defects,” i.e., of defects discovered several releases after their introduction. This can cause a class to be labeled as defect-free while it is not, and is, therefore “snoring.” In this article, we investigate the impact of snoring on classifiers' accuracy and the effectiveness of a possible countermeasure, i.e., dropping too recent data from a training set. We analyze the accuracy of 15 machine learning defect prediction classifiers, on data from more than 4,000 defects and 600 releases of 19 open source projects from the Apache ecosystem. Our results show that on average across projects (i) the presence of dormant defects decreases the recall of defect prediction classifiers, and (ii) removing from the training set the classes that in the last release are labeled as not defective significantly improves the accuracy of the classifiers. In summary, this article provides insights on how to create defects datasets by mitigating the negative effect of dormant defects on defect prediction.

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Pagadala, Srivyshnavi, Sony Bathala, and B. Uma. "An Efficient Predictive Paradigm for Software Reliability." Asian Journal of Computer Science and Technology 8, S3 (June 5, 2019): 114–16. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2051.

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Software Estimation gives solution for complex problems in the software industry which gives estimates for cost and schedule. Software Estimation provides a comprehensive set of tips and heuristics that Software Developers, Technical Leads, and Project Managers can apply to create more accurate estimates. It presents key estimation strategies and addresses particular estimation challenges. In the planning of a software development project, a major challenge faced by project managers is to predict the defects and effort. The Software defect plays critical role in software product development. The estimation of defects can be determined in the product development using many advanced statistical modelling techniques based on the empirical data obtained by the testing phases. The proposed estimation technique in this paper is a model which was developed using Rayleigh function for estimating effect of defects in Software Project Management. The present study offers to decide how many defects creep in to production and determine the effort spent in months. The estimation model was used on Software Testing Life Cycle (STLC) to complete product. The accuracy of the model explains the variation in spent efforts in months associated with number of defects. The model helps the senior management in estimating the defects, schedule, cost and effort.

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Kumar, Swadesh, Rajesh Kumar Singh, and Awadhesh Kumar Maurya. "Software Defect Prediction: State of the Art Survey." International Journal of Innovative Technology and Exploring Engineering 11, no. 7 (June 30, 2022): 32–35. http://dx.doi.org/10.35940/ijitee.g9993.0611722.

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Software has evolved into a critical component in today's world. The quantity of faults in a software product is connected to its quality, which is also restricted by time and cost. In terms of both quality and cost, software faults are costly. The practice of tracing problematic components in software prior to the product's launch is known as software defect prediction. Defects are unavoidable, but we should strive to keep the number of defects to a bare minimum. Defect prediction results in shorter development times, lower costs, less rework, higher customer satisfaction, and more dependable software. As a result, defect prediction procedures are critical for achieving software quality and learning from prior errors.In this study, we conduct a review of the literature from the last two decades and look into recent advancements in the field of defect prediction.

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Misirli, Ayse Tosun, Ayse Bener, and Resat Kale. "AI-Based Software Defect Predictors: Applications and Benefits in a Case Study." AI Magazine 32, no. 2 (June 5, 2011): 57. http://dx.doi.org/10.1609/aimag.v32i2.2348.

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Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The usage of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time and reduces the testing effort. We have built a learning-based defect prediction model for a telecommunication company in the space of one year. In this study, we have briefly explained our model, presented its pay-off and described how we have implemented the model in the company. Furthermore, we compared the performance of our model with that of another testing strategy applied in a pilot project that implemented a new process called Team Software Process (TSP). Our results show that defect predictors can predict 87 percent of code defects, decrease inspection efforts by 72 percent and hence, reduces post-release defects by 44 percent. Furthermore, they can be used as complementary tools for a new process implementation whose effects on testing activities are limited.

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Tosun, Ayse, Ayse Bener, and Resat Kale. "AI-Based Software Defect Predictors: Applications and Benefits in a Case Study." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 2 (July 11, 2010): 1748–55. http://dx.doi.org/10.1609/aaai.v24i2.18807.

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Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The application of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time and reduces the testing effort. We have built a learning-based defect prediction model for a telecommunication company during a period of one year. In this study, we have briefly explained our model, presented its pay-off and described how we have implemented the model in the company. Furthermore, we have compared the performance of our model with that of another testing strategy applied in a pilot project that implemented a new process called Team Software Process (TSP). Our results show that defect predictors can be used as supportive tools during a new process implementation, predict 75% of code defects, and decrease the testing time compared with 25% of the code defects detected through more labor-intensive strategies such as code reviews and formal checklists.

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14

Chou, Chen-Huei. "Metrics in Evaluating Software Defects." International Journal of Computer Applications 63, no. 3 (February 15, 2013): 23–29. http://dx.doi.org/10.5120/10447-5147.

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Anurag, Abhishek, and R. Kamatchi. "A Case Study on Defining a “Quality Algorithm” Based on Correlation between ‘Existing Quality Model, Different Attributes of Defects & Tests’." International Journal of Engineering & Technology 7, no. 3.4 (June 25, 2018): 235. http://dx.doi.org/10.14419/ijet.v7i3.4.16781.

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Usage and nature of software systems have changed significantly. Due to this complexity of software systems has also grown exponentially. In these ever-changing requirements and environment in which software system is being used, maintaining quality of software system is very challenging and difficult. If user requirements are not met as expected, it’s called defect. To improve quality, it’s critical to understand and analyze these defects. In this study root cause analysis technique is used to analyze defects and their attributes, root cause of defects and corrective actions of defects. A quality model is designed based on defects, root cause of defects and tests. A quality algorithm is designed in this study depending on existing quality model, defects, tests and their attributes. This quality algorithm is executed on a software system to validate quality model. The results obtained are analyzed to understand the quality of the software system and how it’s different than existing quality model.

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Bowes, David, Tracy Hall, and Jean Petrić. "Software defect prediction: do different classifiers find the same defects?" Software Quality Journal 26, no. 2 (February 7, 2017): 525–52. http://dx.doi.org/10.1007/s11219-016-9353-3.

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Rana, Rajni, and Dr P. K. Suri. "A Comparative Analysis of Different Clustering Approaches for Software Process Improvement." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 5, no. 1 (August 15, 2013): 404–10. http://dx.doi.org/10.24297/ijmit.v5i1.4500.

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Software development team tries to increase the software quality by decreasing the number of defects as much as possible. Number of defects remaining in a system provides an insight into the quality of the system. Software defects are one of the major factors that can decide the time of software delivery. The proposed system will analyze and categorize the software defects using some cluste ring approach and then the software defects will be measured in each clustered separately. Clustering is the process to present the data in an effective and organized way. There are number of existing clustering approaches but most of them suffer with problem of data distribution. If the distribution is non linear it gives impurities in clustering process. The proposed work is about to use defect prevention for process improvement with the help of clustering algorithms.

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Alyahya, Sultan. "Collaborative Crowdsourced Software Testing." Electronics 11, no. 20 (October 17, 2022): 3340. http://dx.doi.org/10.3390/electronics11203340.

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Crowdsourced software testing (CST) uses a crowd of testers to conduct software testing. Currently, the microtasking model is used in CST; in it, a testing task is sent to individual testers who work separately from each other. Several studies mentioned that the quality of test reports produced by individuals was a drawback because a large number of invalid defects were submitted. Additionally, individual workers tended to catch the simple defects, not those with high complexity. This research explored the effect of having pairs of collaborating testers working together to produce one final test report. We conducted an experiment with 75 workers to measure the effect of this approach in terms of (1) the total number of unique valid defects detected, (2) the total number of invalid defects reported, and (3) the possibility of detecting more difficult defects. The findings show that testers who worked in collaborating pairs can be as effective in detecting defects as an individual worker; the differences between them are marginal. However, CST significantly affects the quality of test reports submitted in two dimensions: it helps reduce the number of invalid defects and also helps detect more difficult defects. The findings are promising and suggest that CST platforms can benefit from new mechanisms that allow for the formation of teams of two individuals who can participate in doing testing jobs.

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Wang, Yi Chen, and Yi Kun Wang. "Solution of Software Test of Computerized Numerical Control (CNC) Systems." Applied Mechanics and Materials 66-68 (July 2011): 1256–59. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.1256.

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Software has been used more and more widely in CNC systems, the defects caused by software among all defects of a CNC device have been higher and higher as well. How to diagnose the defects caused by software in CNC device fast and effectively is the topic of this paper. This paper talks about the features of embedded software first, and then introduces a proved method used in software test and defect diagnosis by using software system test platform. We’ll use an instance to elaborate on it. We’ll make a general summary of this method at the end of the paper.

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SCHNEIDEWIND, NORMAN. "COMPLEXITY-DRIVEN RELIABILITY MODEL." International Journal of Reliability, Quality and Safety Engineering 15, no. 05 (October 2008): 479–94. http://dx.doi.org/10.1142/s0218539308003179.

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A model of software complexity and reliability is developed that uses an evolutionary process to transition from one software system to the next while complexity metrics are used to predict the reliability for each system. Systems are tested until the software passes defect presence criteria and is released. Testing criteria are based on defect count, defect density, and testing efficiency predictions exceeding specified thresholds. In addition, another type of testing efficiency — a directed graph representing the complexity of the software and defects embedded in the code — is used to evaluate the efficiency of defect detection in NASA satellite system software. Complexity metrics were found to be good predictors of defects and testing efficiency in this evolutionary process.

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Almayyan, Waheeda. "Towards Predicting Software Defects with Clustering Techniques." International Journal of Artificial Intelligence & Applications 12, no. 1 (January 31, 2021): 39–54. http://dx.doi.org/10.5121/ijaia.2021.12103.

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The purpose of software defect prediction is to improve the quality of a software project by building a predictive model to decide whether a software module is or is not fault prone. In recent years, much research in using machine learning techniques in this topic has been performed. Our aim was to evaluate the performance of clustering techniques with feature selection schemes to address the problem of software defect prediction problem. We analysed the National Aeronautics and Space Administration (NASA) dataset benchmarks using three clustering algorithms: (1) Farthest First, (2) X-Means, and (3) selforganizing map (SOM). In order to evaluate different feature selection algorithms, this article presents a comparative analysis involving software defects prediction based on Bat, Cuckoo, Grey Wolf Optimizer (GWO), and particle swarm optimizer (PSO). The results obtained with the proposed clustering models enabled us to build an efficient predictive model with a satisfactory detection rate and acceptable number of features.

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CHANG, CHING-PAO, and CHIH-PING CHU. "SOFTWARE DEFECT PREDICTION USING INTERTRANSACTION ASSOCIATION RULE MINING." International Journal of Software Engineering and Knowledge Engineering 19, no. 06 (September 2009): 747–64. http://dx.doi.org/10.1142/s0218194009004428.

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Reducing the variance between expectation and execution of software processes is an essential activity for software development, in which the Causal Analysis is a conventional means of detecting problems in the software process. However, significant effort may be required to identify the problems of software development. Defect prevention prevents the problems from occurring, thus lowering the effort required in defect detection and correction. The prediction model is a conventional means of predicting the problems of subsequent process actions, where the prediction model can be built from the performed actions. This study proposes a novel approach that applies the Intertransaction Association Rule Mining techniques to the records of performed actions in order to discover the patterns that are likely to cause high severity defects. The discovered patterns can then be applied to predict the subsequent actions that may result in high severity defects.

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Prasad, V. S., and K. Sasikala. "A Study On Software Engineering Defect Prediction." Data Analytics and Artificial Intelligence 2, no. 1 (February 1, 2022): 1–6. http://dx.doi.org/10.46632/daai/2/1/1.

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The success of any software system entirely depends on the accuracy of the results of the system and whether it is without any flaws. Software defect prediction problems have an extremely beneficial research potential. Software defects are the major issue in any software industry. Software defects not only reduce the software quality, increase costing but it also suspends the development schedule. Software bugs lead to inaccurate and discrepant results. As an outcome of this, the software projects run late, are cancelled or become unreliable after deployment. Quality and reliability are the major challenges faced in a secure software development process. There are major software cost overruns when a software product with bugs in its various components is deployed at client s side. The software warehouse is commonly used as record keeping repository which is mostly required while adding new features or fixing bugs. Many data mining techniques and dataset repository are available to predict the software defects. Bug prediction technique is an important part in software engineering area for last one decade. Software bugs which detect at early stage are simple and inexpensive for rectifying the software. Software quality can be enhanced by using the bug prediction techniques and the software bug can be reduced if applied accurately. Dependent and independent variable are considered in Software bug prediction. To prevent defect based on software metrics software prediction model are used. Metrics based classification categorize component as defective and non-defective.

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Lee, Dong-Gun, and Yeong-Seok Seo. "Identification of propagated defects to reduce software testing cost via mutation testing." Mathematical Biosciences and Engineering 19, no. 6 (2022): 6124–40. http://dx.doi.org/10.3934/mbe.2022286.

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<abstract> <p>In software engineering, testing has long been a research area of software maintenance. Testing is extremely expensive, and there is no guarantee that all defects will be found within a single round of testing. Therefore, fixing defects that are not discovered by a single round of testing is important for reducing the test costs. During the software maintenance process, testing is conducted within the scope of a set of test cases called a test suite. Mutation testing is a method that uses mutants to evaluate whether the test cases of the test suite are appropriate. In this paper, an approach is proposed that uses the mutants of a mutation test to identify defects that are not discovered through a single round of testing. The proposed method simultaneously applies two or more mutants to a single program to define and record the relationships between different lines of code. In turn, these relationships are examined using the defects that were discovered by a single round of testing, and possible defects are recommended from among the recorded candidates. To evaluate the proposed method, a comparative study was conducted using the fault localization method, which is commonly employed in defect prediction, as well as the Defects4J defect prediction dataset, which is widely used in software defect prediction. The results of the evaluation showed that the proposed method achieves a better performance than seven other fault localization methods (Tarantula, Ochiai, Opt2, Barinel, Dstar2, Muse, and Jaccard).</p> </abstract>

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Vandehei, Bailey, Daniel Alencar Da Costa, and Davide Falessi. "Leveraging the Defects Life Cycle to Label Affected Versions and Defective Classes." ACM Transactions on Software Engineering and Methodology 30, no. 2 (March 2021): 1–35. http://dx.doi.org/10.1145/3433928.

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Two recent studies explicitly recommend labeling defective classes in releases using the affected versions (AV) available in issue trackers (e.g., Jira). This practice is coined as the realistic approach . However, no study has investigated whether it is feasible to rely on AVs. For example, how available and consistent is the AV information on existing issue trackers? Additionally, no study has attempted to retrieve AVs when they are unavailable. The aim of our study is threefold: (1) to measure the proportion of defects for which the realistic method is usable, (2) to propose a method for retrieving the AVs of a defect, thus making the realistic approach usable when AVs are unavailable, (3) to compare the accuracy of the proposed method versus three SZZ implementations. The assumption of our proposed method is that defects have a stable life cycle in terms of the proportion of the number of versions affected by the defects before discovering and fixing these defects. Results related to 212 open-source projects from the Apache ecosystem, featuring a total of about 125,000 defects, reveal that the realistic method cannot be used in the majority (51%) of defects. Therefore, it is important to develop automated methods to retrieve AVs. Results related to 76 open-source projects from the Apache ecosystem, featuring a total of about 6,250,000 classes, affected by 60,000 defects, and spread over 4,000 versions and 760,000 commits, reveal that the proportion of the number of versions between defect discovery and fix is pretty stable (standard deviation <2)—across the defects of the same project. Moreover, the proposed method resulted significantly more accurate than all three SZZ implementations in (i) retrieving AVs, (ii) labeling classes as defective, and (iii) in developing defects repositories to perform feature selection. Thus, when the realistic method is unusable, the proposed method is a valid automated alternative to SZZ for retrieving the origin of a defect. Finally, given the low accuracy of SZZ, researchers should consider re-executing the studies that have used SZZ as an oracle and, in general, should prefer selecting projects with a high proportion of available and consistent AVs.

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CHANG, CHING-PAO. "INTEGRATING ACTION-BASED DEFECT PREDICTION TO PROVIDE RECOMMENDATIONS FOR DEFECT ACTION CORRECTION." International Journal of Software Engineering and Knowledge Engineering 23, no. 02 (March 2013): 147–72. http://dx.doi.org/10.1142/s0218194013500022.

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Reducing software defects is an essential activity for Software Process Improvement. The Action-Based Defect Prediction (ABDP) approach fragments the software process into actions, and builds software defect prediction models using data collected from the execution of actions and reported defects. Though the ABDP approach can be applied to predict possible defects in subsequent actions, the efficiency of corrections is dependent on the skill and knowledge of the stakeholders. To address this problem, this study proposes the Action Correction Recommendation (ACR) model to provide recommendations for action correction, using the Negative Association Rule mining technique. In addition to applying the association rule mining technique to build a High Defect Prediction Model (HDPM) to identify high defect action, the ACR builds a Low Defect Prediction Model (LDPM). For a submitted action, each HDPM rule used to predict the action as a high defect action and the LDPM rules are analyzed using negative association rule mining to spot the rule items with different characteristics in HDPM and LDPM rules. This information not only identifies the attributes required for corrections, but also provides a range (or a value) to facilitate the high defect action corrections. This study applies the ACR approach to a business software project to validate the efficiency of the proposed approach. The results show that the recommendations obtained can be applied to decrease software defect removal efforts.

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Peng, Xuemei. "Research on Software Defect Prediction and Analysis Based on Machine Learning." Journal of Physics: Conference Series 2173, no. 1 (January 1, 2022): 012043. http://dx.doi.org/10.1088/1742-6596/2173/1/012043.

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Abstract The defects of machine learning prediction technology can be more comprehensive and automatic learning model to find the defects in software has become the main method of defect prediction, selection and study of algorithm is the key to improve the accuracy and efficiency of machine learning. Comparing different machine learning defect prediction methods reveals that the algorithms have different advantages in different evaluation indicators, the use of these advantages and combining the stacking ensemble learning method in machine learning is put forward different prediction algorithm of prediction results. As software metrics and again the prediction model of software defect prediction combined machine learning algorithm is based on the experiment with the model of Eclipse, the data sets show the effectiveness of the model.

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Shi, Sheng Li, Jin Shi, and Rui Wang. "A Prediction Model Based on ISOMAP for Software Defects." Applied Mechanics and Materials 347-350 (August 2013): 3278–82. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3278.

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To improve and guarantee the quality of software, it is very necessary to effectively predicting modules with defects in the software. There are usually more measure attributes in software quality prediction, which often leads to the curse of dimension. To do this, a new algorithm based on ISOMAP was presented to predict software defect, which combined manifold learning algorithms and classification methods. In the model, the high dimensional software metrics attribute data were firstly mapped into the low dimensional space through ISOMAP. Then the low dimensional features were classified with KNN, SVM and NB. Experiments demonstrate that the new model progresses the prediction precision of software defects as well as great improves the efficiency of the algorithm.

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Ma, Yanfang, Xiaotong Gao, Wei Zhou, and Liang Chen. "The Trustworthiness Measurement Model of Component Based on Defects." Mathematical Problems in Engineering 2022 (December 12, 2022): 1–15. http://dx.doi.org/10.1155/2022/7290001.

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In modern software engineering, the component-based development approach has become one of the important trends in software development technology. The trustworthiness of components plays a vital role in developing component-based trustworthy software. If there exist defects in components, then the trustworthiness of the component will be reduced, and the trustworthiness of the software system will be influenced. In this case, it is necessary to measure the trustworthiness of the component in terms of the defect. In this paper, a trustworthiness measurement model of components will be proposed based on defects. Firstly, the defect types are formalized according to the component specification. Secondly, the weight allocation method of defect types is designed based on the correlation between defect types and experts’ evaluation. The value of the trustworthiness attribute is estimated by using the risk value of the defect and the weight of the defect type. Furthermore, the trustworthiness measurement model of the component is proposed, the corresponding algorithm is designed, and some algebra properties are proved. Finally, a case study is used to illustrate the application of the model.

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Karnavel, K., and R. Dillibabu. "Development and Application of New Quality Model for Software Projects." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/491246.

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The IT industry tries to employ a number of models to identify the defects in the construction of software projects. In this paper, we present COQUALMO and its limitations and aim to increase the quality without increasing the cost and time. The computation time, cost, and effort to predict the residual defects are very high; this was overcome by developing an appropriate new quality model named the software testing defect corrective model (STDCM). The STDCM was used to estimate the number of remaining residual defects in the software product; a few assumptions and the detailed steps of the STDCM are highlighted. The application of the STDCM is explored in software projects. The implementation of the model is validated using statistical inference, which shows there is a significant improvement in the quality of the software projects.

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Pankov, D. A. "SEARCH SOFTWARE DEFECTS FOR EMBEDDED SYSTEMS." Applied Mathematics and Fundamental Informatics 5, no. 2 (2018): 071–77. http://dx.doi.org/10.25206/2311-4908-2018-5-2-71-77.

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Hongyu Zhang and Sunghun Kim. "Monitoring Software Quality Evolution for Defects." IEEE Software 27, no. 4 (July 2010): 58–64. http://dx.doi.org/10.1109/ms.2010.66.

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Couto, Cesar, Pedro Pires, Marco Tulio Valente, Roberto S. Bigonha, and Nicolas Anquetil. "Predicting software defects with causality tests." Journal of Systems and Software 93 (July 2014): 24–41. http://dx.doi.org/10.1016/j.jss.2014.01.033.

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Westland, J. Christopher. "The cost behavior of software defects." Decision Support Systems 37, no. 2 (May 2004): 229–38. http://dx.doi.org/10.1016/s0167-9236(03)00020-4.

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Alshazly, Amira A., Ahmed M. Elfatatry, and Mohamed S. Abougabal. "Detecting defects in software requirements specification." Alexandria Engineering Journal 53, no. 3 (September 2014): 513–27. http://dx.doi.org/10.1016/j.aej.2014.06.001.

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Hallem, Seth, David Park, and Dawson Engler. "Uprooting Software Defects at the Source." Queue 1, no. 8 (November 2003): 64–71. http://dx.doi.org/10.1145/966712.966722.

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Liu, Zhaohui, Nalini Ravishanker, and Bonnie K. Ray. "NHPP models for categorized software defects." Applied Stochastic Models in Business and Industry 21, no. 6 (2005): 509–24. http://dx.doi.org/10.1002/asmb.604.

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Rainsberger, J. B. "Avoiding Defects." IEEE Software 24, no. 2 (March 2007): 14–15. http://dx.doi.org/10.1109/ms.2007.34.

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YANG, CHI-LU, YEIM-KUAN CHANG, and CHIH-PING CHU. "AN ANALYSIS OF THE ROOT CAUSES OF DEFECTS INJECTED INTO THE SOFTWARE BY THE SOFTWARE TEAM: AN INDUSTRIAL STUDY OF THE DISTRIBUTED HEALTH-CARE SYSTEM." International Journal of Software Engineering and Knowledge Engineering 23, no. 09 (November 2013): 1269–88. http://dx.doi.org/10.1142/s0218194013500393.

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A root cause is a source of software defect, whose removal decreases or removes the defect. A root cause of software defect is injected into the software by software engineers during the development process. One of the main concerns of the software team leader, such as the project manager, is to determine who injected various root causes of the defects into the software and when these have been injected. In this paper, a cost-benefit scheme is presented, which allows a software team to determine skill weakness and improve team capability. The scheme provides effective in-process feedback based on the causal analysis of software defects. The proposed analysis scheme includes orthogonal root cause definitions, role-based root cause types, and gradational correction actions. In the experiment, the projects of a distributed health-care system are used to verify the efficiency of the proposed scheme. The results show that the root cause ratios (RCR) are 33.8%, 30.6%, 21.9%, 10.7%, and 3.0% in design, implementation, analysis, business and deployment, respectively. The defects in the projects mainly occurred during the design and implementation phases of the projects. Correction activities to enhance the designers’ skills, such as exception handling (40.5%) and DB/data schema (25.0%), are the top priorities that must be addressed by the software team. The findings can help the team leader to determine methods to improve these weaknesses.

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Himes, Emma. "NHTSA Up in the Clouds: The Formal Recall Process & Over-the-Air Software Updates." Michigan Technology Law Review, no. 28.1 (2021): 153. http://dx.doi.org/10.36645/mtlr.28.1.nhtsa.

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Software updates are pushed to vehicles “over-the-air” (OTA) with increasing frequency as they reduce costs of visiting dealerships and auto shops to receive maintenance. These updates, pushed from the cloud, have been used to remedy safety defects in vehicles and improve software controlling all aspects of vehicles from steering to rearview mirrors. Remedies of vehicle safety defects are overseen by the National Highway Traffic Safety Administration (NHTSA); however, because many OTA software updates do not remedy issues officially deemed safety defects, they are pushed straight from the manufacturer to drivers with little government oversight or transparency. NHTSA’s recall process was designed in 1966 to remedy safety defects in vehicles, resulting in a process which is now outdated for modern vehicles running on software. NHTSA has acknowledged the increased use of OTA software updates and prescribed OTA remedies for safety defects, but the current framework leaves NHTSA unable to oversee the rapid output of OTA software updates pushed by auto manufacturers. Without updating the current recall process for software related updates to vehicles, and specifically over-the-air software updates, NHTSA’s ability to oversee vehicle safety may decrease and the recall process may grow obsolete as the issues facing vehicles today have changed since Congress defined what constitutes a safety defect.

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Wang, Yan. "Efficient Prediction Method of Defect of Monitor Configuration Software." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 2 (March 20, 2019): 340–44. http://dx.doi.org/10.20965/jaciii.2019.p0340.

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In order to solve the problem of low efficiency in software operation, we need to research the defect prediction of monitoring configuration software. The current method has the low efficiency in the defect prediction of software. Therefore, this paper proposed the software defect prediction method based on genetic optimization support vector machines. This method carried out feature selection for the measure of complexity of software, and built software defect prediction model of genetic optimized support vector machine, and completed the research on the efficient prediction method of software defects. Experimental results show that the proposed method improves the quality of software effectively.

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Manivasagam, G., and R. Gunasundari. "An optimized feature selection using fuzzy mutual information based ant colony optimization for software defect prediction." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 456. http://dx.doi.org/10.14419/ijet.v7i1.1.9954.

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In recent years, there is a significant notification focused towards the prediction of software defect in the field of software engineering. The prediction of software defects assist in reducing the cost of testing effort, improving the process of software testing and to concentrate only on the fault-prone software modules. Recently, software defect prediction is an important research topic in the software engineering field. One of the important factors which effect the software defect detection is the presence of noisy features in the dataset. The objective of this proposed work is to contribute an optimization technique for the selection of potential features to improve the prediction capability of software defects more accurately. The Fuzzy Mutual Information Ant Colony Optimization is used for searching the optimal feature set with the ability of Meta heuristic search. This proposed feature selection efficiency is evaluated using the datasets from NASA metric data repository. Simulation results have indicated that the proposed method makes an impressive enhancement in the prediction of routine for three different classifiers used in this work.

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JALOTE, PANKAJ, ASHOK K. MITTAL, and RAM GOPAL PRAJAPAT. "ON OPTIMUM MODULE SIZE FOR SOFTWARE INSPECTIONS." International Journal of Reliability, Quality and Safety Engineering 14, no. 03 (June 2007): 283–95. http://dx.doi.org/10.1142/s0218539307002659.

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Inspection is widely believed to be one of the most cost-effective methods for detection of defects in the work products produced during software development. However, the inspection process, by its very nature, is labor intensive and for delivering value, they have to be properly executed and controlled. While controlling the inspection process, the inspection module size is a key control parameter. Larger module size can lead to an increased leakage of defects which increases the cost since rework in the subsequent phases is more expensive. Small module size reduces the defect leakage but increases the number of inspections. In this paper, we formulate a cost model for an inspection process using which the total cost can be minimized. We then use the technique of Design of Experiments to study how the optimum module size varies with some of the key parameters of the inspection process, and determine the optimum module size for different situations.

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Wang, Hong, and Limin Yuan. "Software engineering defect detection and classification system based on artificial intelligence." Nonlinear Engineering 11, no. 1 (January 1, 2022): 380–86. http://dx.doi.org/10.1515/nleng-2022-0042.

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Abstract With the increasing reliance on automatic software-based applications, it is important to automate the classification of software defects and ensure software reliability. An automatic software defect classification system based on an expert system is proposed in this article. In this method, DACS first determines the category of software defects through the selection of typical features, then reduces the spatial knowledge base searched by the inference engine and selects the characteristics of a certain type of defect. Make a selection, determine the name of the defect, and finally select different causes and prevention methods for the defect as needed. The DACS structure was built, and the experiment showed that the AI system took 15 s to complete, whereas the traditional mechanism took 48 s; the accuracy of the AI was 99%, whereas the accuracy of the traditional mechanism was only 68%. According to the aforementioned experimental results, the recognition accuracy of the proposed research scheme is higher than that of the traditional mechanism. Hence, the time required to solve the problem of software engineering defect detection and classification is less than that of the traditional mechanism.

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Kumudha, P., and R. Venkatesan. "Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction." Scientific World Journal 2016 (2016): 1–20. http://dx.doi.org/10.1155/2016/2401496.

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Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.

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46

Wang, Fang. "Software Defect Fault Intelligent Location and Identification Method Based on Data Mining." Journal of Physics: Conference Series 2146, no. 1 (January 1, 2022): 012012. http://dx.doi.org/10.1088/1742-6596/2146/1/012012.

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Abstract With the advancement of the times, computer technology is also constantly improving, and people’s requirements for software functions are also constantly improving, and as software functions become more and more complex, developers are technically limited and teamwork is not tacitly coordinated. And so on, so in the software development process, some errors and problems will inevitably lead to software defects. The purpose of this paper is to study the intelligent location and identification methods of software defects based on data mining. This article first studies the domestic and foreign software defect fault intelligent location technology, analyzes the shortcomings of traditional software defect detection and fault detection, then introduces data mining technology in detail, and finally conducts in-depth research on software defect prediction technology. Through in-depth research on several technologies, it reduces the accidents of software equipment and delays its service life. According to the experiments in this article, the software defect location proposed in this article uses two methods to compare. The first error set is used as a unit to measure the subsequent error set software error location cost. The first error set 1F contains 19 A manually injected error program, and the average positioning cost obtained is 3.75%.

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Krepych, R. V., and S. Y. Krepych. "КОМПЛЕКСНЕ ПРОГРАМНЕ ЗАБЕЗПЕЧЕННЯ ЗБОРУ І ВІЗУАЛІЗАЦІЇ СТАТИСТИКИ ДЕФЕКТІВ ПРОГРАМНИХ ПРОЕКТІВ." Information Technology and Computer Engineering 42, no. 2 (2018): 35–42. http://dx.doi.org/10.31649/1999-9941-2018-42-2-35-42.

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Bejjanki, Kiran Kumar, Jayadev Gyani, and Narsimha Gugulothu. "Class Imbalance Reduction (CIR): A Novel Approach to Software Defect Prediction in the Presence of Class Imbalance." Symmetry 12, no. 3 (March 4, 2020): 407. http://dx.doi.org/10.3390/sym12030407.

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Software defect prediction (SDP) is the technique used to predict the occurrences of defects in the early stages of software development process. Early prediction of defects will reduce the overall cost of software and also increase its reliability. Most of the defect prediction methods proposed in the literature suffer from the class imbalance problem. In this paper, a novel class imbalance reduction (CIR) algorithm is proposed to create a symmetry between the defect and non-defect records in the imbalance datasets by considering distribution properties of the datasets and is compared with SMOTE (synthetic minority oversampling technique), a built-in package of many machine learning tools that is considered a benchmark in handling class imbalance problems, and with K-Means SMOTE. We conducted the experiment on forty open source software defect datasets from PRedict or Models in Software Engineering (PROMISE) repository using eight different classifiers and evaluated with six performance measures. The results show that the proposed CIR method shows improved performance over SMOTE and K-Means SMOTE.

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Haider, Syed W., João W. Cangussu, Kendra M. L. Cooper, Ram Dantu, and Syed Haider. "Estimation of Defects Based on Defect Decay Model: ED^{3}M." IEEE Transactions on Software Engineering 34, no. 3 (May 2008): 336–56. http://dx.doi.org/10.1109/tse.2008.23.

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Liu, Can, Sumaya Sanober, Abu Sarwar Zamani, L. Rama Parvathy, Rahul Neware, and Abdul Wahab Rahmani. "Defect Prediction Technology in Software Engineering Based on Convolutional Neural Network." Security and Communication Networks 2022 (April 26, 2022): 1–8. http://dx.doi.org/10.1155/2022/5058461.

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Software defect prediction has become a significant study path in the field of software engineering in order to increase software reliability. Program defect predictions are being used to assist developers in identifying potential problems and optimizing testing resources to enhance program dependability. As a consequence of this strategy, the number of software defects may be predicted, and software testing resources are focused on the software modules with the most problems, allowing the defects to be addressed as soon as feasible. The author proposes a research method of defect prediction technology in software engineering based on convolutional neural network. Most of the existing defect prediction methods are based on the number of lines of code, module dependencies, stack reference depth, and other artificially extracted software features for defect prediction. Such methods do not take into account the underlying semantic features in software source code, which may lead to unsatisfactory prediction results. The author uses a convolutional neural network to mine the semantic features implicit in the source code and use it in the task of software defect prediction. Empirical studies were conducted on 5 software projects on the PROMISE dataset and using the six evaluation indicators of Recall, F1, MCC, pf, gm, and AUC to verify and analyze the experimental results showing that the AUC values of the items varied from 0.65 to 0.86. Obviously, software defect prediction experimental results obtained using convolutional neural networks are still ideal. Defect prediction model in software engineering based on convolutional neural network has high prediction accuracy.

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Journal articles on the topic 'Software defects'

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