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Journal articles on the topic 'Software metrics'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Jiang, Bo, Jiaye Cen, Erluan Zhu, and Jiale Wang. "Software technical debt prediction based on complex software networks." PLOS One 20, no. 6 (2025): e0323672. https://doi.org/10.1371/journal.pone.0323672.

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Technical debt prediction (TDP) is crucial for the long-term maintainability of software. In the literature, many machine-learning based TDP models have been proposed; they used TD-related metrics as input features for machine-learning classifiers to build TDP models. However, their performance is unsatisfactory. Developing and utilizing more effective metrics to build TDP models is considered as a promising approach to enhance the performance of TDP models. Social Network Analysis (SNA) uses a set of metrics (i.e., SNA metrics) to characterize software elements (classes, binaries, etc.) in so

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2

MATSUMOTO, Ken-ichi. "Software Metrics." Journal of Japan Society for Fuzzy Theory and Systems 10, no. 5 (1998): 796–803. http://dx.doi.org/10.3156/jfuzzy.10.5_34.

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3

Kaur, Sandeep. "Software Metrics and Metric Tools A Review." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 4 (2015): 2076–79. http://dx.doi.org/10.17762/ijritcc2321-8169.150468.

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4

Y, Dinesh Kumar, and Nuka Raju Kolli. "Novel Metrics in Software Industry." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (2017): 1252–58. http://dx.doi.org/10.31142/ijtsrd8215.

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5

Leach, Ronald J. "Software metrics and software maintenance." Journal of Software Maintenance: Research and Practice 2, no. 2 (1990): 133–42. http://dx.doi.org/10.1002/smr.4360020204.

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6

Xiang, Yiming, Weifeng Pan, Haibo Jiang, Yunfang Zhu, and Hao Li. "Measuring Software Modularity Based on Software Networks." Entropy 21, no. 4 (2019): 344. http://dx.doi.org/10.3390/e21040344.

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Modularity has been regarded as one of the most important properties of a successful software design. It has significant impact on many external quality attributes such as reusability, maintainability, and understandability. Thus, proposing metrics to measure the software modularity can be very useful. Although several metrics have been proposed to characterize some modularity-related attributes, they fail to characterize software modularity as a whole. A complex network uses network models to abstract the internal structure of complex systems, providing a general way to analyze complex system

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7

Patel, Durga, and Pallavi P. "Software Reliability: Metrics." International Journal of Computer Applications 156, no. 5 (2016): 9–15. http://dx.doi.org/10.5120/ijca2016912433.

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8

Li, W. "Software product metrics." IEEE Potentials 18, no. 5 (2000): 24–27. http://dx.doi.org/10.1109/45.807276.

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9

Meneely, Andrew, Ben Smith, and Laurie Williams. "Validating software metrics." ACM Transactions on Software Engineering and Methodology 21, no. 4 (2012): 1–28. http://dx.doi.org/10.1145/2377656.2377661.

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10

Ince, D. "Software metrics: introduction." Information and Software Technology 32, no. 4 (1990): 297–303. http://dx.doi.org/10.1016/0950-5849(90)90063-w.

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11

Sabbani, Sarachyuth, Kiran Kumar Reddi, and S. V. Achuta Rao. "Software Quality: Issues, Concerns and New Directions." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 8 (2013): 2887–94. http://dx.doi.org/10.24297/ijct.v11i8.3007.

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Software metrics and quality models have a very important role to play in measurement of software quality. A number of well-known quality models and software metrics are used to build quality software both in industry and in academia. Development of software metrics is an ongoing process with new metrics being continuously tried out.Â However, during our research on measuring software quality using object oriented design patterns, we faced many issues related to existing software metrics and quality models. For a particular situation of interest, any established metric can be used. If none is

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12

Chopra, Kunal, and Monika Sachdeva. "EVALUATION OF SOFTWARE METRICS FOR SOFTWARE PROJECTS." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 14, no. 6 (2015): 5845–53. http://dx.doi.org/10.24297/ijct.v14i6.1915.

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Software metrics are developed and used by the many software organizations for the evaluation and confirmation of good code, working and maintenance of the software product. Software metrics measure and identify various types of software complexities such as size metrics, control flow metrics and data flow metrics. One of the significant objective of software metrics is that it is applicable to both a process and product metrics. Ndepend is the most advanced as well as flexible tool available in the market. We have ensured the Quality of the project by using Ndepend metrics. So we have conclud

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13

Shatnawi, Raed, and Alok Mishra. "An Empirical Study on Software Fault Prediction Using Product and Process Metrics." International Journal of Information Technologies and Systems Approach 14, no. 1 (2021): 62–78. http://dx.doi.org/10.4018/ijitsa.2021010104.

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Product and process metrics are measured from the development and evolution of software. Metrics are indicators of software fault-proneness and advanced models using machine learning can be provided to the development team to select modules for further inspection. Most fault-proneness classifiers were built from product metrics. However, the inclusion of process metrics adds evolution as a factor to software quality. In this work, the authors propose a process metric measured from the evolution of software to predict fault-proneness in software models. The process metrics measures change-prone

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14

Turan, Metin. "Integrating Software Metrics with UML Class Diagrams." Lecture Notes on Software Engineering 3, no. 3 (2015): 220–24. http://dx.doi.org/10.7763/lnse.2015.v3.194.

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15

Kim, Jungho, Sungwon Kang, Jongsun Ahn, and Seonah Lee. "EMSA: Extensibility Metric for Software Architecture." International Journal of Software Engineering and Knowledge Engineering 28, no. 03 (2018): 371–405. http://dx.doi.org/10.1142/s0218194018500134.

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Software extensibility, the capability of adding new functions to a software system, is established based on software architecture. Therefore, developers need to evaluate the capability when designing software architecture. To support the evaluation, researchers have proposed metrics based on quality models or scenarios. However, those metrics are vague or subjective, depending on specific systems and evaluators. We propose the extensibility metric for software architecture (EMSA), which represents the degree of extensibility of a software system based on its architecture. To reduce the subjec

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16

Schneidewind, Norman. "Updated Software Reliability Metrics." Journal of Aerospace Computing, Information, and Communication 6, no. 5 (2009): 354–72. http://dx.doi.org/10.2514/1.40883.

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17

Baranov, Sergey Nikolaevich, and Alexander Mikhailovich Telezhkin. "Metrics for Software Development." SPIIRAS Proceedings 5, no. 36 (2014): 5. http://dx.doi.org/10.15622/sp.36.1.

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18

Prather, Ronald E. "On hierarchical software metrics." Software Engineering Journal 2, no. 2 (1987): 42. http://dx.doi.org/10.1049/sej.1987.0006.

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19

Grady, R. B. "Successfully applying software metrics." Computer 27, no. 9 (1994): 18–25. http://dx.doi.org/10.1109/2.312034.

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20

Fenton, Norman E., and Agnes A. Kaposi. "Metrics and software structure." Information and Software Technology 29, no. 6 (1987): 301–20. http://dx.doi.org/10.1016/0950-5849(87)90029-2.

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21

Cook, Curtis R., and Andreas Roesch. "Real-time software metrics." Journal of Systems and Software 24, no. 3 (1994): 223–37. http://dx.doi.org/10.1016/0164-1212(94)90065-5.

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22

Inglis, James. "Standard Software Quality Metrics." AT&T Technical Journal 65, no. 2 (1986): 113–18. http://dx.doi.org/10.1002/j.1538-7305.1986.tb00299.x.

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23

Dinesh, Kumar Y., and Raju Kolli Nuka. "Novel Metrics in Software Industry." International Journal of Trend in Scientific Research and Development 2, no. 1 (2017): 1252–58. https://doi.org/10.31142/ijtsrd8215.

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The role of metrics in software quality is well recognized. However, software metrics are yet to be standardized and integrated into development practices across software industry. While process, project, and product metrics share a common goal of contributing to software quality and reliability, utilization of metrics has been at minimum. This work is an effort to bring more attention to software metrics. It examines the practices of metrics in software industry and the experiences of some organizations that have developed, promoted, and utilized variety of software metrics. As various types

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24

Najadat, Hassan, Izzat Alsmadi, and Yazan Shboul. "Predicting Software Projects Cost Estimation Based on Mining Historical Data." ISRN Software Engineering 2012 (April 10, 2012): 1–8. http://dx.doi.org/10.5402/2012/823437.

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In this research, a hybrid cost estimation model is proposed to produce a realistic prediction model that takes into consideration software project, product, process, and environmental elements. A cost estimation dataset is built from a large number of open source projects. Those projects are divided into three domains: communication, finance, and game projects. Several data mining techniques are used to classify software projects in terms of their development complexity. Data mining techniques are also used to study association between different software attributes and their relation to cost

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25

Srinivasan, K. P., and T. Devi. "Software Metrics Validation Methodologies in Software Engineering." International Journal of Software Engineering & Applications 5, no. 6 (2014): 87–102. http://dx.doi.org/10.5121/ijsea.2014.5606.

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26

Aggarwal, K. K., Yogesh Singh, Arvinder Kaur, and Ruchika Malhotra. "Software Design Metrics for Object-Oriented Software." Journal of Object Technology 6, no. 1 (2007): 121. http://dx.doi.org/10.5381/jot.2007.6.1.a4.

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27

Belachew, Ermiyas Birihanu. "Analysis of Software Quality Using Software Metrics." International Journal on Computational Science & Applications 8, no. 4/5 (2018): 11–20. http://dx.doi.org/10.5121/ijcsa.2018.8502.

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28

Johari, Kalpana, and Arvinder Kaur. "Effect of software evolution on software metrics." ACM SIGSOFT Software Engineering Notes 36, no. 5 (2011): 1–8. http://dx.doi.org/10.1145/2020976.2020987.

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29

THONGMAK, MATHUPAYAS, and PORNSIRI MUENCHAISRI. "MAINTAINABILITY METRICS FOR ASPECT-ORIENTED SOFTWARE." International Journal of Software Engineering and Knowledge Engineering 19, no. 03 (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

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30

Zhong, Yiwen, Kun Song, ShengKai Lv, and Peng He. "An Empirical Study of Software Metrics Diversity for Cross-Project Defect Prediction." Mathematical Problems in Engineering 2021 (November 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/3135702.

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Cross-project defect prediction (CPDP) is a mainstream method estimating the most defect-prone components of software with limited historical data. Several studies investigate how software metrics are used and how modeling techniques influence prediction performance. However, the software’s metrics diversity impact on the predictor remains unclear. Thus, this paper aims to assess the impact of various metric sets on CPDP and investigate the feasibility of CPDP with hybrid metrics. Based on four software metrics types, we investigate the impact of various metric sets on CPDP in terms of F-measu

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31

Concas, Giulio, Michele Marchesi, Alessandro Murgia, and Roberto Tonelli. "An Empirical Study of Social Networks Metrics in Object-Oriented Software." Advances in Software Engineering 2010 (November 29, 2010): 1–21. http://dx.doi.org/10.1155/2010/729826.

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We study the application to object-oriented software of new metrics, derived from Social Network Analysis. Social Networks metrics, as for instance, the EGO metrics, allow to identify the role of each single node in the information flow through the network, being related to software modules and their dependencies. These metrics are compared with other traditional software metrics, like the Chidamber-Kemerer suite, and software graph metrics. We examine the empirical distributions of all the metrics, bugs included, across the software modules of several releases of two large Java systems, Eclip

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32

Vidanapathirana, V.S.P, and K.H.M.R Peiris. "Software Metrics for Identifying Software Size in Software Development Projects." COMPUSOFT: An International Journal of Advanced Computer Technology 04, no. 08 (2015): 1960–65. https://doi.org/10.5281/zenodo.14789605.

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Measurements are fundamental any engineering discipline. They indicate the amount, extent, dimension or capacity of an attribute or a product, in a quantitative manner. The analyzed results of the measured data can be given as the basic idea of metrics. It is a quantitative representation of the measurements of the degree to which a system, component, or process possesses a given attribute. When it comes to software, the metrics are a wide scope of measurements of computer programming. The size oriented metrics takes a main role in it since they can be used as the key for better estimations, t

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33

Ajith Jubilson, E., and Ravi Sankar Sangam. "Software Metrics for Computing Quality of Software Agents." Journal of Computational and Theoretical Nanoscience 17, no. 5 (2020): 2035–38. http://dx.doi.org/10.1166/jctn.2020.8845.

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Metrics are the essential building blocks for any evaluation process. They establish specific goals for improvement. Multi agent system (MAS) is complex in nature, due to the increase in complexity of developing a multi agent system, the existing metrics are less sufficient for evaluating the quality of an MAS. This is due to the fact that agent react in an unpredictable manner. Existing metrics for measuring MAS quality fails to addresses potential communication, initiative behaviour and learn-ability. In this work we have proposed additional metrics for measuring the software agent. A softwa

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34

Yakovyna, Vitalii, and Bohdan Uhrynovskyi. "User-perceived Response Metrics in Android OS for Software Aging Detection." Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 9 (June 10, 2021): 32–43. http://dx.doi.org/10.23939/sisn2021.09.032.

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Mobile systems and devices including Android are vulnerable to the effects of software aging which are manifested in performance degradation during long run-time. It is important to identify efficient system and user interface metrics for detecting and counteracting the software aging effects. The aging metrics used in researches of the Android operating system do not take into account the aging processes in user applications. Therefore, this paper discusses two new graphical user interface metrics that allow to track performance degradation and user applications response time: Frame Draw Time

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35

Zaim, Amirul, Johanna Ahmad, Noor Hidayah Zakaria, Goh Eg Su, and Hidra Amnur. "Software Defect Prediction Framework Using Hybrid Software Metric." JOIV : International Journal on Informatics Visualization 6, no. 4 (2022): 921. http://dx.doi.org/10.30630/joiv.6.4.1258.

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Software fault prediction is widely used in the software development industry. Moreover, software development has accelerated significantly during this epidemic. However, the main problem is that most fault prediction models disregard object-oriented metrics, and even academician researcher concentrate on predicting software problems early in the development process. This research highlights a procedure that includes an object-oriented metric to predict the software fault at the class level and feature selection techniques to assess the effectiveness of the machine learning algorithm to predic

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36

Haner Kırğıl, Elif Nur, and Tülin Erçelebi Ayyıldız. "Predicting Software Cohesion Metrics with Machine Learning Techniques." Applied Sciences 13, no. 6 (2023): 3722. http://dx.doi.org/10.3390/app13063722.

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The cohesion value is one of the important factors used to evaluate software maintainability. However, measuring the cohesion value is a relatively difficult issue when tracing the source code manually. Although there are many static code analysis tools, not every tool measures every metric. The user should apply different tools for different metrics. In this study, besides the use of these tools, we predicted the cohesion values (LCOM2, TCC, LCC, and LSCC) with machine learning techniques (KNN, REPTree, multi-layer perceptron, linear regression (LR), support vector machine, and random forest

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37

Udara, Rangika Herath, De Silva Dilshan, Godapitiya Virajini, Navoda Wanni Arachchige Piumi, Kotuwe Gedara Heshan, and Premadasa Rashmi. "A Comparative Study of Different Software Complexity Metrics in Measuring Software Interoperability." Indian Journal of Software Engineering and Project Management (IJSEPM) 3, no. 2 (2023): 1–4. https://doi.org/10.54105/ijsepm.B7652.073223.

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<strong>Abstract: </strong>Software interoperability is crucial for organizations that rely on multiple software systems to perform their operations. However, due to the complexity and variety of software systems, ensuring interoperability can be difficult. Measuring software complexity metrics can be used to identify potential problems and assess how well different interoperability strategies work. In this study, we investigated and compared the effectiveness of different software complexity metrics in measuring software interoperability. We used statistical methods to analyze data collected

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38

Mishra, Alok, Raed Shatnawi, Cagatay Catal, and Akhan Akbulut. "Techniques for Calculating Software Product Metrics Threshold Values: A Systematic Mapping Study." Applied Sciences 11, no. 23 (2021): 11377. http://dx.doi.org/10.3390/app112311377.

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Several aspects of software product quality can be assessed and measured using product metrics. Without software metric threshold values, it is difficult to evaluate different aspects of quality. To this end, the interest in research studies that focus on identifying and deriving threshold values is growing, given the advantage of applying software metric threshold values to evaluate various software projects during their software development life cycle phases. The aim of this paper is to systematically investigate research on software metric threshold calculation techniques. In this study, el

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39

Malhotra, Ruchika, and Megha Khanna. "Analyzing Evolution Patterns of Object-Oriented Metrics." International Journal of Rough Sets and Data Analysis 6, no. 3 (2019): 49–66. http://dx.doi.org/10.4018/ijrsda.2019070104.

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Software evolution is mandatory to keep it useful and functional. However, the quality of the evolving software may degrade due to improper incorporation of changes. Quality can be monitored by analyzing the trends of software metrics extracted from source code as these metrics represent the structural characteristics of a software such as size, coupling, inheritance etc. An analysis of these metric trends will give insight to software practitioners regarding effects of software evolution on its internal structure. Thus, this study analyzes the trends of 14 object-oriented (OO) metrics in a wi

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40

Sudhaman, Parthasarathy. "Evaluating software quality using software metrics for a software product." International Journal of Information Systems and Change Management 5, no. 2 (2011): 180. http://dx.doi.org/10.1504/ijiscm.2011.041514.

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41

Kratz, Benedikt, Ralf Reussner, and Willem-Jan van den Heuvel. "EMPIRICAL RESEARCH ON SIMILARITY METRICS FOR SOFTWARE COMPONENT INTERFACES." Journal of Integrated Design and Process Science: Transactions of the SDPS, Official Journal of the Society for Design and Process Science 8, no. 4 (2004): 1–17. http://dx.doi.org/10.3233/jid-2004-8401.

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In this article we define metrics for measuring component interface similarity. These metrics were applied in an empirical study investigating the relationship between the functionality and the interfaces of software components. The main contributions of this article are: (i) the introduction of software component interface refactorings, i.e., transformations working on IDL-style (i.e., signature-list based) component interfaces. These refactorings are used to define a similarity metric for signature-list based component interfaces, (ii) the definition of efficiently computable metrics measuri

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42

Alenezi, Mamdouh, and Ibrahim Abunadi. "Evaluating Software Metrics as Predictors of Software Vulnerabilities." International Journal of Security and Its Applications 9, no. 10 (2015): 231–40. http://dx.doi.org/10.14257/ijsia.2015.9.10.21.

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43

Wiper, M. P., A. P. Palacios, and J. M. Marin. "Bayesian Software Reliability Prediction Using Software Metrics Information." Quality Technology & Quantitative Management 9, no. 1 (2012): 35–44. http://dx.doi.org/10.1080/16843703.2012.11673276.

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44

Ryan, Linda. "Software usage metrics for real-world software testing." Computer Standards & Interfaces 21, no. 2 (1999): 127. http://dx.doi.org/10.1016/s0920-5489(99)92033-0.

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45

Schneidewind, Norman F. "Software metrics validation: Space Shuttle flight software example." Annals of Software Engineering 1, no. 1 (1995): 287–309. http://dx.doi.org/10.1007/bf02249054.

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46

SHEPPERD, MARTIN J., and DARREL C. INCE. "SOFTWARE METRICS IN SOFTWARE ENGINEERING AND ARTIFICIAL INTELLIGENCE." International Journal of Software Engineering and Knowledge Engineering 01, no. 04 (1991): 463–76. http://dx.doi.org/10.1142/s0218194091000305.

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This paper examines the utility of much of the software metrics research that has been carried out in software engineering to problems in artificial intelligence. The paper first reviews the work that has been carried out and then makes a number of suggestions about how it could be transferred to the artificial intelligence arena — applying it in particular to expert system development.

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47

Khoshgoftaar, T. M., and J. C. Munson. "Predicting software development errors using software complexity metrics." IEEE Journal on Selected Areas in Communications 8, no. 2 (1990): 253–61. http://dx.doi.org/10.1109/49.46879.

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48

Ryan, L. "Software usage metrics for real-world software testing." IEEE Spectrum 35, no. 4 (1998): 64–68. http://dx.doi.org/10.1109/6.666964.

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49

Tran, Tuyet-Lan, and Yosef S. Sherif. "Software metrics: Measuring the progress of software development." Microelectronics Reliability 28, no. 5 (1988): 693–702. http://dx.doi.org/10.1016/0026-2714(88)90005-4.

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50

Stein, Cara, Letha Etzkorn, Sampson Gholston, et al. "SEMANTIC METRICS: METRICS BASED ON SEMANTIC ASPECTS OF SOFTWARE." Applied Artificial Intelligence 23, no. 1 (2009): 44–77. http://dx.doi.org/10.1080/08839510802573574.

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