Journal articles: 'Maintainability model'

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Liu, Tie Lin, and Cheng Zhang. "Study on Concurrent Maintainability Design Based on Pro/INTRALINK." Applied Mechanics and Materials 470 (December 2013): 452–55. http://dx.doi.org/10.4028/www.scientific.net/amm.470.452.

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In order to make product maintainable, maintainability engineering encourages sufficient focus on product maintenance at early development stage, and systematic maintainability design throughout product development and design. Maintainability and other life cycle aspects should be concurrently design into product. Integrated maintainability information model is established. Based on product assembly information model, the components of integrated maintainability information model, and how to integrate this model with design process model are discussed respectively. With Pro/INTRALINK as product data management platform, an integrated environment which supports concurrent maintainability design and its establishment steps are explored, including configuring basic environment, establishing master information model, defining and managing design flow, which provide and effective methods for concurrent maintainability design.

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Park, Mikyeong, and Euyseok Hong. "Classification model for maintainability prediction." Contemporary Engineering Sciences 7 (2014): 1691–97. http://dx.doi.org/10.12988/ces.2014.411227.

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Meier, John R., and Jeffrey S. Russell. "Model Process for Implementing Maintainability." Journal of Construction Engineering and Management 126, no. 6 (December 2000): 440–50. http://dx.doi.org/10.1061/(asce)0733-9364(2000)126:6(440).

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Tarelko, Wieslaw. "Control model of maintainability level." Reliability Engineering & System Safety 47, no. 2 (January 1995): 85–91. http://dx.doi.org/10.1016/0951-8320(94)00055-s.

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Liu, Hui, and Jian Ping Hao. "Study of the Model and Algorithm in Linear Programming of Maintainability Growth Project." Applied Mechanics and Materials 271-272 (December 2012): 1427–33. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.1427.

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In order to solve the planning problem of maintainability growth project, study of the model and algorithm in linear programming of maintainability growth project is put forward. Firstly, the linear relationship of factor and target as an example, the mathematical model of maintainability growth project planning is put forward in the article, and a general solution method is mentioned. Then according to the maintainability growth problem’s characteristic, the algorithm is improved. A solution method is brought out which is about simplex method with variable changed form 0 to 1- γi. This Algorithm makes the solution of maintainability growth project planning become much simpler than before.

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Yadav, Rohit, and Raghuraj Singh. "A Hyper-parameter Tuning based Novel Model for Prediction of Software Maintainability." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 2 (March 10, 2023): 106–13. http://dx.doi.org/10.17762/ijritcc.v11i2.6134.

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Software maintainability is regarded as one of the most important characteristics of any software system. In today's digital world, the expanding significance of software maintenance is motivating the development of efficient software maintainability prediction (SMP) models using statistical and machine learning methods. This study proposes a hyper-parameter optimizable Software Maintainability Prediction (HPOSMP) model using the hybridized approach of data balancing and hyper-parameter optimization of Machine Learning (ML) approach using software maintainability datasets. The training dataset has been created with object-oriented software namely UIMS and QUES. To balance the dataset, Synthetic Minority Oversampling Technique (SMOTE) technology has been adopted. Further, Decision Tree, Gaussian Naïve Bayes, K-Nearest neighbour, Logistic Regression, and Support Vector Machine are adopted as Machine Learning and Statistical Regression Techniques for training of software maintainability dataset. Results demonstrate that the proposed HPOSMP model gives better performance as compared to the base SMP models.

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Chen, Wen Hong, and Xiao Wei Wang. "A Way to Predict and Evaluate of Software Maintainability Based on Machine Learning." Advanced Materials Research 926-930 (May 2014): 2924–27. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2924.

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The accurate maintainability prediction and evaluation of software applications can improve the designing management for these applications, thus benefiting designing organizations. Therefore, there is considerable research interest in development and application of sophisticated techniques which can be used to build models for both predicting and evaluating software maintainability. In this paper, we investigate some ideas based on Machine Learning, Natural Language Processing, Fuzzy Logic, and Systematic Model of Software Maintenance. The idea to compute Interactive Index and the maintainability of software system is useful to study the relation between maintainability prediction and maintainability evaluation in the whole software process. An model basing on fuzzy matrix and BP neural network is built up. It’s approved that there are application value of using this model based on BP neural network to predict and evaluate the software maintainability.

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De Silva, Nayanthara, Malik Ranasinghe, and Chathura Ranjan De Silva. "Risk analysis in maintainability of high-rise buildings under tropical conditions using ensemble neural network." Facilities 34, no. 1/2 (February 1, 2016): 2–27. http://dx.doi.org/10.1108/f-05-2014-0047.

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Purpose – The aim of this research study is to develop a risk-based framework that can quantify maintainability to forecast future maintainability of a building at early stages as a decision tool to minimize increase of maintenance cost. Design/methodology/approach – A survey-based approach was used to explore the risk factors in the domain of maintainability risks under tropical environmental conditions. The research derived ten risk factors based on 58 identified causes related to maintainability issues as common to high-rise buildings in tropical conditions. Impact of these risk factors was evaluated using an indicator referred to as the “maintenance score (MS)” which was derived from the “whole-life maintenance cost” involved in maintaining the expected “performance” level of the building. Further, an ensemble neural network (ENN) model was developed to model the MS for evaluating maintainability risks in high-rise buildings. Findings – Results showed that predictions from the model were highly compatible and in the same order when compared with calculations based on actual past data. It further showed that, maintainability of buildings could be improved if the building was designed, constructed and managed properly by controlling their maintainability risks. Originality/value – The ENN model was used to analyze maintainability of a high-rise building. Thus, it provides a useful tool for designers, clients, facilities managers/maintenance managers and users to analyze maintainability risks of buildings at early stages.

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Wu, Jia Ju, Bing Ning Cao, Zheng Jing Wan, and Ji Lan Zhang. "The Application of BP Neural Network to Comprehensive Assessment in Equipment Maintainance Support." Applied Mechanics and Materials 190-191 (July 2012): 191–94. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.191.

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Equipment maintainability support is the most important influencing factor which affects weapon battle ability. Equipment maintainability support capability assessment result can be used as steering for equipment maintenance blue print configuration. Based on analysis of equipment maintenance support, maintenance scenario establish process is discussed in paper. Then neural network-based equipment maintainability support assessment model is established. BP dynamic neural network is applied in constructing model and the amelioration weight reset arithmetic is imported in equipment maintainability support assessment model. Finally, the steps of equipment maintenance support assessment are described. According to assessment arithmetic, equipment maintenance center computes assessment result. Equipment development department and army can optimize maintainability support scheme based on assessment result and maintain equipment.

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Zhu, Lian Jun, and Hua Cong. "The Prediction of the Maintainability of Armored Vehicle Engine Based on Failure Model Effectiveness Analysis." Advanced Materials Research 988 (July 2014): 601–5. http://dx.doi.org/10.4028/www.scientific.net/amr.988.601.

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Maintainability is an important factor need to be considered in the strategy making process of armored vehicle engine. Combined with the factors analysis of the maintainability, fuzzy analytic hierarchy process (FAHP) is used to realize the reasonable evaluation of the engine maintainability based on failure model effectiveness analysis (FMEA) and the characteristic parameter test. The research provides a basis for the condition based maintenance of armored vehicle engine.

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Luo, Xu, Yong Min Yang, Zhe Xue Ge, and Xi Sen Wen. "Evaluation and Decision-Making Method for Maintainability Design Based on Fuzzy Preference." Applied Mechanics and Materials 401-403 (September 2013): 2242–46. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.2242.

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Considering the uncertainty of maintainability design factors and the fuzzy preference information in evaluation values, an evaluation and decision-making method of maintainability was proposed. The linguistic assessment values of maintainability design factors were converted to trapezoid fuzzy numbers by fuzzy set theory, and decision weights under fuzzy preference were given based on maximizing deviation method. Then, evaluation model of maintainability design was established, and confidence index of evaluating decision was given. At last, a case study was given and the result showed that the method is reasonable and effective for maintainability evaluation.

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Jain, Parita, Arun Sharma, and Laxmi Ahuja. "Software Maintainability Estimation in Agile Software Development." International Journal of Open Source Software and Processes 9, no. 4 (October 2018): 65–78. http://dx.doi.org/10.4018/ijossp.2018100104.

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Agile methodologies have gained wide acceptance for developing high-quality products with a quick and flexible approach. However, until now, the quality of the agile process has not been validated quantitatively. Quality being important for the software system, there is a need for measurement. Estimating different quality factors will lead to a quality product. Also, agile software development does not provide any precise models to evaluate maintainability. Therefore, there is a need for an algorithmic approach that can serve as the basis for estimation of maintainability. The article proposes an adaptive neuro-fuzzy inference system (ANFIS) model for estimating agile maintainability. Maintainability is one of the prominent quality factors in the case of agile development. The proposed model has been verified and found to be effective for assessing the maintainability of agile software.

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Zhu, Chun Sheng, Qi Zhang, Fan Tun Su, and Hong Liang Ran. "Research on the Multi-Objective Optimization Model of System-Level BIT Testability Index Determination." Applied Mechanics and Materials 121-126 (October 2011): 2223–27. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.2223.

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By weighing reliability, maintainability, availability and life-cycle cost of equipment which are influenced by testability，the testability indexes of system level BIT are determined on the basis of maximum system reliability & maintainability and minimum the life-circle cost. The influence mathematical models of system reliability, maintainability, availability and life-circle cost are established. According to these mathematical models, the multi-objective optimization model of system-level BIT testability indexes is established. The multi-objective optimization model is solved using Non-dominated Sorting Genetic Algorithm II, and the validity of the multi-objective optimization model is proved through an example.

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Chen, Xingxing, Mingyu Feng, and Rujuan Zhao. "Aircraft maintainability verification evaluation based on fuzzy analytic hierarchy process." Journal of Physics: Conference Series 2764, no. 1 (May 1, 2024): 012057. http://dx.doi.org/10.1088/1742-6596/2764/1/012057.

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Abstract This paper uses the fuzzy analytic hierarchy process to establish the mathematical model of aircraft maintainability verification in the flight test stage. The maintainability evaluation index system is constructed from the factors that affect the aircraft maintainability level, such as equipment layout and accessibility, cover design, marking and error prevention, standardization and interchangeability, and human factor engineering. Through case analysis, the application of this method in the maintainability verification of the military aircraft flight test phase is introduced.

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Jin, Liying, Wensheng Wang, HouYong Shu, Xuemei Ma, Chenxing Liang, and Qiang Ma. "Application Research on the Maintainability Allocation Method of a Certain Shooter Seat." Mathematical Problems in Engineering 2021 (December 29, 2021): 1–10. http://dx.doi.org/10.1155/2021/9376450.

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In view of the traditional maintainability allocation method for a certain shooter seat for maintainability allocation did not consider the lifecycle expense problem, the improved NSGA-II algorithm (iNSGA-II, for short) is adopted to establish a multiobjective comprehensive trade-off model for a certain shooter seat product lifecycle maintenance-related expenses and mean time to repair (MTTR, for short) and construct multiobjective optimization problem. The experimental results show that the Pareto optimal solution effectively solves the limitation of the traditional maintainability allocation method and then provides a basis for a certain shooter seat to obtain a reasonable maintainability allocation scheme. The superiority of the iNSGA-II algorithm to optimize the maintainability allocation of a certain shooter seat was verified by comparing it with the traditional maintainability allocation method.

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Ajiboye, Mary Adebola, Matthew Sunday Abolarin, Johnson Adegbenga Ajiboye, Abraham Usman Usman, and Sanjay Misra. "Pair Programming – Cubic Prediction Model Results for Random Pairs and Individual Junior Programmers." Engineering World 5 (November 6, 2023): 163–66. http://dx.doi.org/10.37394/232025.2023.5.18.

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Due to the rapidly evolving technology in the dynamic world, there is a growing desire among software clients for swift delivery of high-quality software. Agile software development satisfies this need and has been widely and appropriately accepted by software professionals. The maintainability of such software, however, has a significant impact on its quality. Unfortunately, existing works neglected to consider timely delivery and instead concentrated primarily on the flexibility component of maintainability. This research looked at maintainability as a function of time to rectify codes among Individual Junior and Random pair software developers. Data was acquired from an experiment performed on software developers in the agile environment and analyzed to develop the quality model metrics for maintainability which was used for prediction. One hundred programmers each received a set of agile codes created in the Python programming language, with deliberate bugs ranging from one to ten. The cubic regression model was used for predicting time spent on debugging errors above ten bugs. Results show that the random pair programmers spent an average time of 21.88 min/error while the individual programmers spent a lesser time of 16.57 min/error.

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Lu, Zhong, Jie Liu, Li Dong, and Xihui Liang. "Maintenance Process Simulation Based Maintainability Evaluation by Using Stochastic Colored Petri Net." Applied Sciences 9, no. 16 (August 9, 2019): 3262. http://dx.doi.org/10.3390/app9163262.

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Maintainability is a critical design characteristic of products. Maintainability evaluation plays an important role in maintainability design. Existing maintainability evaluation approaches neglect logistic effects on system maintainability. In this paper, tuples of stochastic colored petri nets (SCPN) are used to express the constituents of a maintenance process; and the maintenance process model is developed based on the SCPN. Approaches for calculating the required maintenance resources are proposed according to the state equation of the SCPN, and a method for calculating maintenance time is proposed by using an SCPN based discrete-event simulation. Finally, the line maintenance of a wheel steering system is used as a case study to illustrate the application and effectiveness of our proposed approaches. The work discussed herein provides a maintainability evaluation methodology based on the maintenance task demonstration that is conducted on the digital mockup of products. The approaches can be applied in the design stage when there are no physical mockups, and the maintainability design can be carried on concurrently with the development of products.

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Zighed, Narimane, Nora Bounour, and Abdelhak-Djamel Seriai. "Comparative Analysis of Object-Oriented Software Maintainability Prediction Models." Foundations of Computing and Decision Sciences 43, no. 4 (December 1, 2018): 359–74. http://dx.doi.org/10.1515/fcds-2018-0018.

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Abstract Software maintainability is one of the most important aspects when evaluating the quality of a software product. It is defined as the ease with which the existing software can be modified. In the literature, several researchers have proposed a large number of models to measure and predict maintainability throughout different phases of the Software Development Life Cycle. However, only a few attempts have been made for conducting a comparative study of the existent proposed prediction models. In this paper, we present a detailed classification and conduct a comparative analysis of Object-Oriented software maintainability prediction models. Furthermore, we considered the aforementioned proposed models from three perspectives, which are architecture, design and code levels. To the best of our knowledge, such an analysis that comprises the three levels has not been conducted in previous research. Moreover, this study hints at certain fundamental basics concerning the way of how measure the maintainability knowing that at each level the maintainability will be measured differently. In addition, we will focus on the strengths and weaknesses of these models. Consequently, the comparative study yields that several statistical and machine learning techniques have been employed for software maintainability prediction at code level during the last decade, and each technique possesses its specific characteristic to develop an accurate prediction model. At the design level, the majority of the prediction models measured maintainability according to the characteristics of the quality models. Whereas at the architectural level, the techniques adopted are still limited and only a few of studies have been conducted in this regard.

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Zhang, Wei, Zhen Yu Ma, Qing Ling Lu, Xiao Zhen Wang, and Da Wei Liu. "A Method of Software Maintainability Evaluation Based on Static Analysis." Applied Mechanics and Materials 651-653 (September 2014): 1757–60. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.1757.

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Static analysis tools can generate plenty of information about the software. This paper uses the information to build the evaluation metrics of software maintainability, and unified these metrics. Then, we put forward a calculating method of the weights of metrics and create a software maintainability evaluation model. The results of software evaluation show that this method can evaluate the software maintainability objectively and qualitatively, and provides guide for decision-making and execution of software maintenance.

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THONGMAK, MATHUPAYAS, and PORNSIRI MUENCHAISRI. "MAINTAINABILITY METRICS FOR ASPECT-ORIENTED SOFTWARE." International Journal of Software Engineering and Knowledge Engineering 19, no. 03 (May 2009): 389–420. http://dx.doi.org/10.1142/s0218194009004234.

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Maintainability is an important factor that developers should be concerned because two-thirds of software costs involve maintenance. Aspect-oriented programming (AOP) paradigm is aimed to increase the software maintainability. It solves code tangling and code scattering problems by introducing a new modular unit, called "aspect". Various research works are provided to support measuring the object-oriented software, but only few studies are set up to support measuring the aspect-oriented software. This paper proposes aspect-oriented software maintainability metrics and a set of aspect-oriented design guidelines to support the metrics. By combining the proposed guidelines, object-oriented design principles, and aspect-oriented design principles, the metrics are constructed according to the Factor-Strategy (FS) quality model and the Factor-Criteria-Metric (FCM) quality model. Principle violation check definitions in the form of Boolean expressions are also defined to conduct software measurement and to fulfill the metrics. Finally, the aspect-oriented software maintainability metrics are applied to detect design principle violations in fifty AspectJ systems. The results show that for all systems their hidden flaws are exposed. Moreover, the proposed metrics are used to compare the maintainability between two versions of systems written in Java and AspectJ.

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Li, Ruiying, Xiaoxi Liu, and Ning Huang. "Availability Allocation of Networked Systems Using Markov Model and Heuristics Algorithm." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/315385.

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It is a common practice to allocate the system availability goal to reliability and maintainability goals of components in the early design phase. However, the networked system availability is difficult to be allocated due to its complex topology and multiple down states. To solve these problems, a practical availability allocation method is proposed. Network reliability algebraic methods are used to derive the availability expression of the networked topology on the system level, and Markov model is introduced to determine that on the component level. A heuristic algorithm is proposed to obtain the reliability and maintainability allocation values of components. The principles applied in the AGREE reliability allocation method, proposed by the Advisory Group on Reliability of Electronic Equipment, and failure rate-based maintainability allocation method persist in our allocation method. A series system is used to verify the new algorithm, and the result shows that the allocation based on the heuristic algorithm is quite accurate compared to the traditional one. Moreover, our case study of a signaling system number 7 shows that the proposed allocation method is quite efficient for networked systems.

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Asadi, Morteza, and Hassan Rashidi. "A Model for Object-Oriented Software Maintainability Measurement." International Journal of Intelligent Systems and Applications 8, no. 1 (January 8, 2016): 60–66. http://dx.doi.org/10.5815/ijisa.2016.01.07.

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Aggarwal, K. K., Yogesh Singh, Pravin Chandra, and Manimala Puri. "Measurement of Software Maintainability Using a Fuzzy Model." Journal of Computer Science 1, no. 4 (April 1, 2005): 538–42. http://dx.doi.org/10.3844/jcssp.2005.538.542.

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Al-Sarayreh, Khalid T., Alain Abran, and Juan J. Cuadrado-Gallego. "A standards-based model of system maintainability requirements." Journal of Software: Evolution and Process 25, no. 5 (March 14, 2012): 459–505. http://dx.doi.org/10.1002/smr.1553.

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Wu, Xi, Bao Qi Wang, Da Xu, and Ge Mu. "The Method of Equipment Average Replacement TimeVerification Based on Virtual Simulation." Applied Mechanics and Materials 347-350 (August 2013): 2846–49. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2846.

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Aiming to lack for the verification method of equipment maintainability quantitative requirements, this paper researched the verification method of average replacement time through virtual simulation technology, and proposed the mathematical model of average replacement time, which can lay the foundation for maintainability virtual verification.

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Pal Dhaka, Dr Vijay, and Swati Agrawal. "Machine Learning Algorithm Evaluate the maintainability." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 2 (August 10, 2013): 1376–83. http://dx.doi.org/10.24297/ijct.v10i2.7008.

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Maintainability is an important quality attribute and a difficult concept as it involves a number of measurements. Quality estimation means estimating maintainability of software. Maintainability is a set of attribute that bear on the effort needed to make specified modification. The main goal of this paper is to propose use of few machine learning algorithms with an objective to predict software maintainability and evaluate them. The propose models are Gaussian process regression networks (GPRN), probably approximately correct learning (PAC), Genetic algorithm (GA). This paper predicts the maintenance effort. The QUES (Quality evaluation system) dataset are used in this study. The QUES datasets contains 71 classes. To measure the maintainability, number of “CHANGE” is observed over a period of few years. We can define CHANGE as the number of lines of code which were added, deleted or modified during few year maintenance periods. After this study these machine learning algorithm was compared with few models such as GRNN (General regression neural network) model, RT (Regression tree), MARS (Multiple adaptive regression splines), SVM (Support vector machine), MLR (Multiple linear regression) models. Based on experiments, it was found that GPRN can be predicting the maintainability more accurately and precisely than prevailing models. We also include object oriented software metric to measure the software maintainability. The use of machine learning algorithms to establish the relationship between metrics and maintainability would be much better approach as these are based on quantity as well as quality.

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He, Xue Hong, Zhi Liang Hu, Xi Jie Zhai, and Li Yang Xie. "A Maintenance Policy Model Based on Time-Variant Reliability." Applied Mechanics and Materials 556-562 (May 2014): 3760–67. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3760.

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A maintenance policy model based on time-variant reliability is put forward upon those mechanical systems which can carry on multiple maintenance in the whole life period, and the recurrence relations of the preventive maintenance intervals and reliability benefits is established. Two kinds of devices, one device’s mean time to failure complies with exponential distribution and another device’s mean time to failure meets the Weibull distribution, are taken as examples to get the approximate function expressions of the system's maintainability parameters of these devices through the curve fitting for data, analyze the system’s maintainability parameters and get the equipment’s maintenance policy model based on time-variant reliability. Examples verify the feasibility of the model.

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Xue, Yu Xia, Gui Xiang Shen, Ying Zhi Zhang, and Li Quan Guo. "Maintainability Modeling of NC Machine Tools Based on Repair Time." Advanced Materials Research 1006-1007 (August 2014): 494–97. http://dx.doi.org/10.4028/www.scientific.net/amr.1006-1007.494.

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According to 62 field fault data from a certain type of 8 NC machine tools, maintainability distribution model is established based on the repair time. Hypothesis testing shows that the repair times of NC machine tools fit mathematical model of lognormal distribution. This provides the basis for maintainability assessment of NC machine tool as well as the development of maintenance strategy in the future.

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Et. al., Surender Singh,. "Fuzzy Based Stable Maintainability Metric for Software Projects." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 500–509. http://dx.doi.org/10.17762/turcomat.v12i2.860.

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To make software better in view of its maintainability, its software development process must be controlled and continuously observed. Researchers and software managers have stressed on the early measurement of maintainability starting from design phase itself so that timely steps could be taken for producing maintainable software. This paper evaluates and compares several methodologies for improving the numerical stability of a fuzzy-logic-based maintainability metrics system. Fuzzy parameters are adjusted using heuristic methods. A number of alternates were considered, in which training data sets were generated using different methods and these sets were used to evaluate objective functions in GA and accordingly fuzzy parameters were tuned. After conditioning, real projects’ maintainability data is used to show that fuzzy model performance is increased, however marginally, after conditioning

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Ge, Zhexue, Yi Zhang, Yongmin Yang, Xu Luo, Qiang Li, and Fang Wang. "A New Maintainability Evaluation Method Based on Virtual-real Fusion Scene Construction." Scientific Programming 2022 (March 9, 2022): 1–12. http://dx.doi.org/10.1155/2022/6547225.

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Maintainability is one important attribute of product quality. Maintainability evaluation is important to reduce maintenance cost and improve availability. With this research work, an effective maintainability evaluation method is proposed by mingling virtual environment and real world physical equipment. In order to reduce the cost of maintainability test scenario construction, the surrounding environment of physical equipment is replaced by virtual model for virtual-real fusion. The virtual environment is mainly used to simulate the impact on maintenance operation space. The difficulty of realizing the test concept deals with how to accurately identify the pose of the physical equipment and register the virtual environment around the physical equipment. In the proposed method, initially the ORB feature of the physical product is extracted through binocular vision. Secondly, the ICP method is used to match the physical product feature with the digital prototype feature, so as to identify the relative pose of the physical equipment. The virtual maintenance environment is then accurately superimposed. Thirdly, the experimental evaluation method of virtual reality fusion maintainability qualitative and quantitative indicators is formulated. Finally, taking an engine as an example, a virtual-real fusion maintainability test case study is carried out to verify the effectiveness and feasibility of maintainability evaluation based on virtual-real fusion test scenario. With 5 test groups, evaluation is carried out in three scenes-real environment, virtual-real fusion and without surrounding environment. Results of the evaluation revealed that error rate of the proposed virtual reality fusion maintainability method are somehow closer to maintainability in real environment. The average error rate of the three maintainability qualitative indexes in the virtual-real fusion scene is 9.90%, whereas the average error of the maintenance time is 3.25%. The proposed system offers an absorbing simulation environment where users are privileged to interact with the maintenance objects. The research work bears great significance in maintenance, product designing, visualization and related domains. As maintenance in real environment happens to be costlier and hazardous, the proposed method is suitable to ensure substantial cost reduction and safety in all stages of maintainability. Moreover, outcomes of the evaluation and the generated data of simulation can be generalized to improve maintainability of equipment.

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Liang, Ruoxi, and Qunfeng Ye. "An Immersive Virtual Maintenance Simulation Process for Civil Aircraft Based on Cavernous Automatic Virtual System." Journal of Physics: Conference Series 2220, no. 1 (March 1, 2022): 012011. http://dx.doi.org/10.1088/1742-6596/2220/1/012011.

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Abstract This paper discusses the immersive virtual maintenance simulation in the design and verification phases of civil aircraft. Firstly, the concepts of maintainability design and Virtual maintenance are introduced. Secondly, the workflow of immersive Virtual maintenance simulation based on cavernous automatic virtual environment (CAVE) system for civil aircraft is discussed in detail. Through the computer virtual model to simulate and estimate the product maintainability and maintenance support system and other aspects of the possible problems, improve prediction and decision-making level, make maintainability design technology out of the dependence on experience, develop to a new stage of all-round prediction. Finally, the development trend of civil aircraft maintainability design and immersive virtual maintenance simulation method is discussed, and some suggestions on the research and application of this field are put forward.

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Khalek, Imad A., Jad M. Chalhoub, and Steven K. Ayer. "Augmented Reality for Identifying Maintainability Concerns during Design." Advances in Civil Engineering 2019 (March 3, 2019): 1–12. http://dx.doi.org/10.1155/2019/8547928.

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In a building context, decisions made early in the design phase can have a major impact on maintainability of the resulting facility. Effectively leveraging the knowledge of facility management teams in the design stage can lead to improved maintainability in the operation phase, but this feedback can be challenging to elicit during the design stage because facility management teams may not be formed by the time of design. This requires designers, who may not have facility management experience, to think like facility managers in order to consider the needs of the maintenance teams. This paper examines the extent to which different visualization media may be able to enable individuals without prior maintenance experience to identify maintainability concerns in a design model. Student participants, without prior maintenance experience, were randomly assigned to explore a design to assess maintainability concerns with either augmented reality (AR) or a traditional computer screen for viewing a Building Information Model (BIM). Their perceptions, behaviors, and statements were recorded and analyzed. Results indicate that BIM supports better identification of potentially problematic areas, but AR allows users to more consistently determine why an area is problematic. This suggests an opportunity to use a hybrid BIM/AR approach for identifying and resolving maintainability considerations during the design phase. The findings from this work provide evidence to illustrate how AR and BIM may enable individuals with limited experience to be able to effectively think like facility managers in order to make better maintainability decisions during design to lead to a better building during operation.

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Ma, Sa Sa, Chao Wei Zhang, Ying Xin Xu, and Shi Ying Yang. "Application of TRIZ Substance-Field Analysis Model in Equipment’s Combat Resilience Design." Advanced Materials Research 1037 (October 2014): 111–16. http://dx.doi.org/10.4028/www.scientific.net/amr.1037.111.

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In order to meet the design requirement of maintainability in equipment’s battlefield guarantee process, substance-field analysis model based on the TRIZ theory is introduced in this paper. It can improve system’s maintainability design efficiency to predict the evolution direction of technical system and analysis the ideal solution by the model. Taking the design of combat resilience of radar feed transmission system as an example, this paper analyzed the application of substance-field model in equipment’s combat resilience design. The result shows that TRIZ substance-field analysis model can effectively guide the combat resilience design, specify the design direction, and improve the design efficiency.

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Saltz, Jeffrey S., Kevin Crowston, Robert Heckman, and Yatish Hegde. "MIDST: an enhanced development environment that improves the maintainability of a data science analysis." International Journal of Information Systems and Project Management 8, no. 3 (September 21, 2021): 5–22. http://dx.doi.org/10.12821/ijispm080301.

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With the increasing ability to generate actionable insight from data, the field of data science has seen significant growth. As more teams develop data science solutions, the analytical code they develop will need to be enhanced in the future, by an existing or a new team member. Thus, the importance of being able to easily maintain and enhance the code required for an analysis will increase. However, to date, there has been minimal research on the maintainability of an analysis done by a data science team. To help address this gap, data science maintainability was explored by (1) creating a data science maintainability model, (2) creating a new tool, called MIDST (Modular Interactive Data Science Tool), that aims to improve data science maintainability, and then (3) conducting a mixed method experiment to evaluate MIDST. The new tool aims to improve the ability of a team member to update and rerun an existing data science analysis by providing a visual data flow view of the analysis within an integrated code and computational environment. Via an analysis of the quantitative and qualitative survey results, the experiment found that MIDST does help improve the maintainability of an analysis. Thus, this research demonstrates the importance of enhanced tools tohelp improve the maintainability of data science projects.

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Yadav, Rohit, and Raghuraj Singh. "Enhancing Software Maintainability Prediction Using Multiple Linear Regression and Predictor Importance." International Journal of Experimental Research and Review 36 (December 30, 2023): 135–46. http://dx.doi.org/10.52756/ijerr.2023.v36.013.

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Accurate maintenance effort and cost estimation are essential for effective software development. By identifying software modules with poor maintainability, Software Maintainability Prediction (SMP) plays a crucial role in managing software maintenance expenses. Previous research efforts have used multiple regression techniques to predict software maintainability, but the results regarding various accuracy and performance metrics are inconclusive. As such, developing a methodology that can recommend regression techniques for software maintainability prediction in the face of inconsistent performance or accuracy metrics is imperative. This research addresses the critical issue of software maintainability and presents a novel approach, the Software Maintainability Model (SMP) utilizing the Predictor Importance (PI) Method, Multiple Linear Regression (MLR), and five machine learning techniques. The proposed SMP integrates ten static source code metrics from object-oriented programming. MLR and PI implement feature selection, and the SMP's performance is evaluated based on accuracy and the Mean Magnitude of Relative Error (MMRE) parameters. Our findings are promising: for the User Interface Management System (UIMS) software, the proposed SMP demonstrates an impressive MMRE of 0.2441 and an accuracy of 91.91%. Similarly, for the Quality Evaluation System (QUES) software, an MMRE value of 0.2222 is achieved alongside a maximum accuracy of 80.95%. The ensemble method, when compared to other Machine Learning (ML) techniques, exhibits superior performance. These results affirm the effectiveness of our approach, contributing to the enhancement of software maintainability in object-oriented programming systems.

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Feng, Hui Juan, Le Chen, and Shi Wei Zhao. "Simulation Model of Aggregated Equipment Combat Unit’s Mission Maintainability in the Mobile Mission." Applied Mechanics and Materials 121-126 (October 2011): 4700–4705. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4700.

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The equipment combat unit is usually in the aggregated state when operate mission, and the mission maintainability is an important factor for its operational effectiveness. There were kinds of parameter to measure the mission maintainability, such as Mean Downtime and Mission Time to Restore Function. Based on the analyses to layers and performance of the aggregated equipment combat units, the signification of aggregated equipment combat units’ Mission Time to Restore Function was adopted and analyzed, the simulation method and simulation flow for the mobile mission of aggregated equipment combat units were given, and then the simulation model under ExSpect was constructed. Finally, an example was used to prove the simulation model.

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Liu, Yifan, John I. Messner, and Robert M. Leicht. "A process model for usability and maintainability design reviews." Architectural Engineering and Design Management 14, no. 6 (August 20, 2018): 457–69. http://dx.doi.org/10.1080/17452007.2018.1512042.

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Zhong, Lu, and Sun Youchao. "Research on Maintainability Evaluation Model Based on Fuzzy Theory." Chinese Journal of Aeronautics 20, no. 5 (October 2007): 402–7. http://dx.doi.org/10.1016/s1000-9361(07)60061-2.

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Liu, Yong Po, Chuang Ye Chang, Shuang Mei Liu, and Ji Wu. "Discovery of Invocation Model and Dynamic Test Configuration Model Based on TTCN-3 Test Systems." Advanced Materials Research 765-767 (September 2013): 1704–8. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1704.

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Aimed at the comprehensibility, reusability and maintainability, the thesis presents the reverse model recovery for the legacy code developed by TTCN-3. It can also help tester and maintainers to verify the test implement, etc. The thesis introduces the discovery of invocation model and dynamic test configuration model based on the reverse model discovery system framework.

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Ossai, EO. "Maintainability of manpower system with restricted recruitment." Global Journal of Mathematical Sciences 12, no. 1 (January 19, 2015): 1–4. http://dx.doi.org/10.4314/gjmas.v12i1.11.

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The maintainability of a manpower system is studied under a Markov framework. The classical method of controlling only one factor of flow is extended to highlight the case in which two factors are under control simultaneously. One special case of this extension, where recruitment of units faces partial embargo, is given, which led to other important results.KEY WORDS: Manpower structure, Maintainability, Factors of flow, Markov model Probability.

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Malhotra, Ruchika, and Anuradha Chug. "Software Maintainability: Systematic Literature Review and Current Trends." International Journal of Software Engineering and Knowledge Engineering 26, no. 08 (October 2016): 1221–53. http://dx.doi.org/10.1142/s0218194016500431.

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Software maintenance is an expensive activity that consumes a major portion of the cost of the total project. Various activities carried out during maintenance include the addition of new features, deletion of obsolete code, correction of errors, etc. Software maintainability means the ease with which these operations can be carried out. If the maintainability can be measured in early phases of the software development, it helps in better planning and optimum resource utilization. Measurement of design properties such as coupling, cohesion, etc. in early phases of development often leads us to derive the corresponding maintainability with the help of prediction models. In this paper, we performed a systematic review of the existing studies related to software maintainability from January 1991 to October 2015. In total, 96 primary studies were identified out of which 47 studies were from journals, 36 from conference proceedings and 13 from others. All studies were compiled in structured form and analyzed through numerous perspectives such as the use of design metrics, prediction model, tools, data sources, prediction accuracy, etc. According to the review results, we found that the use of machine learning algorithms in predicting maintainability has increased since 2005. The use of evolutionary algorithms has also begun in related sub-fields since 2010. We have observed that design metrics is still the most favored option to capture the characteristics of any given software before deploying it further in prediction model for determining the corresponding software maintainability. A significant increase in the use of public dataset for making the prediction models has also been observed and in this regard two public datasets User Interface Management System (UIMS) and Quality Evaluation System (QUES) proposed by Li and Henry is quite popular among researchers. Although machine learning algorithms are still the most popular methods, however, we suggest that researchers working on software maintainability area should experiment on the use of open source datasets with hybrid algorithms. In this regard, more empirical studies are also required to be conducted on a large number of datasets so that a generalized theory could be made. The current paper will be beneficial for practitioners, researchers and developers as they can use these models and metrics for creating benchmark and standards. Findings of this extensive review would also be useful for novices in the field of software maintainability as it not only provides explicit definitions, but also lays a foundation for further research by providing a quick link to all important studies in the said field. Finally, this study also compiles current trends, emerging sub-fields and identifies various opportunities of future research in the field of software maintainability.

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Afiyanti, Lufia, and Nurkhamid. "Analisis dan Pengembangan Sistem Informasi Posyandu Berbasis Web Studi Kasus Posyandu Handayani." Journal of Information Engineering and Technology (JIETY) 1, no. 1 (March 1, 2023): 9–17. http://dx.doi.org/10.21831/jiety.v1i1.35.

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Penelitian pengembangan ini memiliki tujuan untuk mengembangkan sistem informasi Posyandu berbasis web untuk menyelesaikan studi kasus di Posyandu Handayani Dusun Gunung Kelir dalam pembuatan laporan. Dalam penelitian ini mencangkup proses pengembangan sistem informasi dan pengujian kualitas sistem menggunakan alpha dan beta testing serta mengikuti standar ISO 25010 pada aspek fungsional suitability, usability, reliability, maintainability, performance efficiency. Penelitian ini menggunakan metode Research and Development (R&D) dengan model pengembangan waterfall. Subjek dari penelitian ini kader Posyandu Handayani. Instrument functional suitability, usability dan maintainability menggunakan angket dengan pengisian ceklist sedangkan pada aspek reliability menggunakan tools WAPT 10.1 dan aspek performance efficiency menggunakan tools GTMetrix. Setelah melalui tahap pengujian dari sistem informasi Posyandu yang dibuat dapat diambil kesimpulan bahwa Sistem informasi Posyandu berbasis web telah melalui model pengembangan Waterfall sesuai dengan urutan dan kualitas sistem informasi Posyandu dapat memenuhi standar ISO 25010 pada aspek fungsional suitability, usability, reliability, maintainability, performance efficiency.

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Restu, Indra. "Pengembangan Sistem Informasi Sewa Mobil Mini Bus Rute Makassar - Bone Berbasis Android." Journal of Embedded Systems, Security and Intelligent Systems 2, no. 2 (December 1, 2021): 75. http://dx.doi.org/10.26858/jessi.v2i2.24218.

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Penelitian ini bertujuan untuk menghasilkan sisteminformasi pelayanan sewa mobil rute Makassar-Bone dan untuk mengetahui tanggapan pengguna terhadap sistem informasi sewa mobil ini. Jenis penelitian yang digunakan adalah Research and Development dan menggunakan model pengembangan yaitu model Prototyping. Uji kelayakan perangkat lunak berdasar ISO 9126, yakni karakteristik functionality,usability,portability,Maintainability,efficiency.Hasil penelitian ini adalah sebuah sistempelayanan sewa mobil mini bus rute Makassar-Bone berbasis android yang merupakan sebuah sistem informasi mengenai daftar mobil sewa yang ada di Kota Makassar dan Kabupaten Bone untuk mempermudah pengguna dalam hal ini pemesanan mobil sewa tanpa ke lokasi mobil sewa tersebut dan driver dapat mengelola pesanan secara akurat. Sistem informasi ini bersifat online. Pengujian perangkat lunak ISO 9126 dengan empat aspek yaitu functionality dengan presentase penilaian 100% dengan kategori kelayakan dapat diterima, aspek usability dari 20 responden 95% menunjukan interpretasi sangat baik, portability dengan presentase kelayakan 100% dari 3 pengujian di versi android berbeda dan telah memenuhi aspek portability, dan Maintainability telah memenuhi standar Maintainability.

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Nguyen, Tien Long, Yi Qiang Wang, and Yan Hui Zhao. "The Application of RAM Analysis in a CNC System." Advanced Materials Research 591-593 (November 2012): 1701–9. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1701.

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The reliability, availability and maintainability (RAM) analysis of a computerized numerical control (CNC) system is helpful for performing design modifications that are required to achieve minimum failures or to increase the mean time between failures (MTBF), to plan maintainability requirements, to optimize reliability and to maximize equipment availability. To demonstrate these benefits, this paper presents the application of RAM analysis in a CNC system. The Weibull approach is used to model the CNC system behavior. After determining the steady state solution for the CNC system behavior, the corresponding values of reliability and maintainability are estimated at different mission times. The computed results proved to be helpful for analyzing the CNC system behavior, which enabled a considerable improvement of the system performance by implementing suitable maintenance strategies.

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Soltanali, Hamzeh, A. H. S. Garmabaki, Adithya Thaduri, Aditya Parida, Uday Kumar, and Abbas Rohani. "Sustainable production process: An application of reliability, availability, and maintainability methodologies in automotive manufacturing." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 4 (December 31, 2018): 682–97. http://dx.doi.org/10.1177/1748006x18818266.

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Automotive manufacturing industries are required to improve their productivity with higher production rates at the lowest cost, less number of unexpected shutdowns, and reliable operation. In order to achieve the above objectives, the application of reliability, availability, and maintainability methodologies can constitute for resilient operation, identifying the bottlenecks of manufacturing process and optimization of maintenance actions. In this article, we propose a framework for reliability, availability, and maintainability evaluation and maintenance optimization to improve the performance of conveying process of vehicle body in an automotive assembly line. The results of reliability, availability, and maintainability analysis showed that the reliability and maintainability of forklift and loading equipment are the main bottlenecks. To find the optimal maintenance intervals of each unit, a multi-attribute utility theory is applied for multi-criteria decision model considering reliability, availability, and costs. Due to the series configuration of conveying process in automotive assembly line, the optimized time intervals are obtained using opportunistic maintenance strategy. The results could be useful to improve operational performance and sustainability of the production process.

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Berciu, Liviu-Marian, and Vasilica Moldovan. "Software Maintainability and Refactorings Prediction Based on Technical Debt Issues." Studia Universitatis Babeș-Bolyai Informatica 68, no. 2 (December 22, 2023): 22–40. http://dx.doi.org/10.24193/subbi.2023.2.02.

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Software maintainability is a crucial factor impacting cost, time and resource allocation for software development. Code refactorings greatly enhance code quality, readability, understandability and extensibility. Hence, accurate prediction methods for both maintainability and refactorings are vital for long-term project sustainability and success, offering substantial benefits to the software community as a whole. This article focuses on prediction of software maintainability and the number of needed code refactorings using technical debt data. Two approaches were explored, one compressing technical debt issues per software component and employing machine learning algorithms such as ExtraTrees, Random Forest, Decision Trees, which all obtained a high accuracy and performance. The second approach retained multiple debt issue entries and utilized a Recurrent Neural Network, although less effectively. In addition to the prediction of the requisite number of code refactorings and software maintainability for individual software components, a comprehensive analysis of technical debt issues was conducted before and after the refactoring process. The outcomes of this study contribute to the advancement of a dependable prediction system for maintainability and refactorings, presenting potential advantages to the software community in effectively managing maintenance resources. Of all the employed models, the ExtraTrees model yielded the most optimal predictive outcomes. To the best of our knowledge no other approaches of using ML techniques for this problem have been reported in the literarture. Keywords and phrases: Software Quality, Sonarqube, Refactoring, Code Smells.

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Alshammari, Turki, and Mohammad Alshayeb. "Toward a Software Bad Smell Prioritization Model for Software Maintainability." Arabian Journal for Science and Engineering 46, no. 9 (June 9, 2021): 9157–77. http://dx.doi.org/10.1007/s13369-021-05766-6.

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Govil, K. K. "A simple model for life cycle cost vs maintainability function." Microelectronics Reliability 32, no. 1-2 (January 1992): 269–70. http://dx.doi.org/10.1016/0026-2714(92)90107-v.

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Tsantas, N., and A. C. Georgiou. "Partial maintainability of a population model in a stochastic environment." Applied Stochastic Models and Data Analysis 13, no. 3-4 (September 1997): 183–89. http://dx.doi.org/10.1002/(sici)1099-0747(199709/12)13:3/4<183::aid-asm311>3.0.co;2-r.

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Mukunga, Catherine Wambui, John Gichuki Ndia, and Geoffrey Mariga Wambugu. "A METRICS -BASED MODEL FOR ESTIMATING THE MAINTENANCE EFFORT OF PYTHON SOFTWARE." International Journal of Software Engineering & Applications 14, no. 3 (May 26, 2023): 15–29. http://dx.doi.org/10.5121/ijsea.2023.14302.

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Software project management includes a substantial area for estimating software maintenance effort. Estimation of software maintenance effort improves the overall performance and efficiency of software. The Constructive Cost Model (COCOMO) and other effort estimation models are mentioned in literature but are inappropriate for Python programming language. This research aimed to modify the Constructive Cost Model (COCOMO II) by considering a range of Python maintenance effort influencing factors to get estimations and incorporated size and complexity metrics to estimate maintenance effort. A within-subjects experimental design was adopted and an experiment questionnaire was administered to forty subjects aiming to rate the maintainability of twenty Python programs. Data collected from the experiment questionnaire was analyzed using descriptive statistics. Metric values were collected using a developed metric tool. The subject ratings on software maintainability were correlated with the developed model’s maintenance effort, a strong correlation of 0.610 was reported meaning that the model is valid.

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Journal articles on the topic 'Maintainability model'

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