Main Article Content

Abstract

The use of metrics is important in software development activities as they make it possible to check quality, identify failures and other benefits. The objective of this paper is to propose a new software metric based on a bibliometric study and a literature review on software metrics. The bibliometric research was carried out in the Scopus and Web of Science databases to identify the distribution of articles by year of publication, the main authors, affiliation, country, the most common languages, the types of documents, journals with more publications, areas of knowledge, and the keyword clusters. Twenty-three articles were subsequently selected for reading to compose the literature review. The results of the bibliometric research show that (i) there is no defined core of research; (ii) there is a fluctuation of the number of published articles; (iii) the predominant language is English, and the country with the highest index of publications is the United States; (iv) the main area of knowledge is computer science; (v) in relation to affiliation, Florida Atlantic University stands out; (vi) the journal with the largest number of publications is the Journal of Systems and Software. The literature review showed that many software metrics can be used for different purposes, but most of them are related to code, and none are related to acceptance. As such, a support metric for the software acceptance process is proposed to facilitate the delivery phase of the software product, providing security for the customer and cost savings for the developing company.

Keywords

software metrics bibliometric literature review software quality

Article Details

How to Cite
dos Santos Barcelos, M. R. ., Simões Gomes, C. F. ., Manzolillo Sanseverino, A., & dos Santos, M. . (2021). Literature review on software metrics and a New Proposal. nline erspectives: xact ∓ ngineering, 11(32), 33–59. https://doi.org/10.25242/885X113220212284

References

  1. ABES. (2020). Brazilian Association of Software Companies. Available at: <http://www.abessoftware.com.br>. June 2020.
  2. ABNT: Brazilian Association of Technical Standards. NBR ISO/IEC 9126-1: Software Engineering - Product Quality”. Part 1: Quality Model, 2003.
  3. ALQMASE, M.; ALSHAYEB, M.; GHOUTI, L. Threshold Extraction Framework for Software Metrics. Journal of Computer Science and Technology, v.34, n.5, p.1063-1078, 2019.
  4. AMARA, D.; RABAI, L.B.A. Towards a new framework of software reliability measurement based on software metrics. Procedia Computer Science, v.109, p.725-730, 2017.
  5. ARVANITOU, E.M. et al. Software metrics fluctuation: a property for assisting the metric selection process. Information and Software Technology, v.72, p.110-124, 2016.
  6. AVERSANO, L. et al. Investigating Differences and Commonalities of Software Metric Tools, 2017, In: ICSOFT. p.249-256.
  7. BERANIČ, T.; HERIČKO, M. Comparison of systematically derived software metrics thresholds for object-oriented programming languages. Computer Science and Information Systems, v.17, n.1, p.181-203, 2019.
  8. BHARDWAJ, M.; RANA, A. Key Software Metrics and its Impact on each other for Software Development Projects. ACM SIGSOFT Software Engineering Notes, v.41, n.1, p.1-4, 2016.
  9. BOUCHER, A.; BADRI, M. Software metrics thresholds calculation techniques to predict fault-proneness: An empirical comparison. Information and Software Technology, v.96, p.38-67, 2018.
  10. BOZZELLI, P.; GU, Q.; LAGO, P. A systematic literature review on green software metrics, 2013, VU University, Amsterdam.
  11. COSTA. H.G. Model for webibliomining: proposal and application. Revista da FAE, v.13, n.1, p.115-126, 2010.
  12. DA SILVA, F.E. et al. Uso de software como suporte às atividades de gestão da qualidade. Exatas & Engenharias, v.9, n.26, p.45-54, 2019.
  13. DE PÁDUA PAULA FILHO, W. Engenharia de software [Software Engineering]. LTC, 2003.
  14. DEY, T.; MOCKUS, A. Deriving a usage-independent software quality metric. Empirical Software Engineering, v.25, n.2, p.1596-1641, 2020.
  15. DÓSEA, M.; SANT'ANNA, C.; DA SILVA, B.C. How do design decisions affect the distribution of software metrics? In: Proceedings of the 26th Conference on Program Comprehension. ACM, 2018, p.74-85.
  16. FENTON, N.; BIEMAN, J. Software metrics: A rigorous and practical approach. CRC Press, 2014.
  17. FREITAS, N.N.; MONTEIRO, S.B.S. Simulação computacional como ferramenta de suporte a decisão. Exatas & Engenharias, v.7, n.17, p.98-113, 2017.
  18. FREITAS, R.X.; ALBUQUERQUE, A.B.; SANTOS, R. A utilização de guias, normas e modelos de maturidade como apoio à aquisição e contratação de tecnologia da informação em instituições públicas brasileiras [The use of guides, standards and maturity models to support the acquisition and hiring of information technology in Brazilian public institutions]. In: XIII Workshp anual do MPS (WAMPS), 2017.
  19. HASSAN, R. et al. Usability Requirements Extraction Method from Software Document. International Journal of Software Engineering and Knowledge Engineering, v.30, n.2, p.171-189, 2020.
  20. ISHIKIRIYAMA, C.S.; MIRO, D.; GOMES, C.F.S. Text Mining Business Intelligence: A small sample of what words can say. Procedia Computer Science, v.55, p.261-267, 2015.
  21. KARANATSIOU, D. et al. A bibliometric assessment of software engineering scholars and institutions (2010–2017). Journal of Systems and Software, v.147, p.246-261, 2019.
  22. LAKSHMI, P.; MAHESWARI, T.L. An effective rank approach to software defect prediction using software metrics. In: 10th International Conference on Intelligent Systems and Control (ISCO). 2016, IEEE, p.1-5.
  23. LINCKE, R.; LUNDBERG, J.; LÖWE, W. Comparing software metrics tools. In: Proceedings of the 2008 international symposium on Software testing and analysis. 2008. p.131-142.
  24. LIU, Y. et al. Connecting software metrics across versions to predict defects, 2018, In: IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, p.232-243.
  25. LUDWIG, J.; XU, S.; WEBBER, F. Static software metrics for reliability and maintainability. In: IEEE/ACM International Conference on Technical Debt (TechDebt). IEEE, 2018. p.53-54.
  26. MALHOTRA, R.; SHARMA, A. Estimating the threshold of software metrics for web applications. International Journal of System Assurance Engineering and Management, p.1-16, 2019.
  27. MAUŠA, G., GRBAC, T.G. The stability of threshold values for software metrics in software defect prediction, 2017. In: International Conference on Model and Data Engineering. Springer, Cham. p.81-95.
  28. MEDEIROS, N. et al. Software metrics as indicators of security vulnerabilities. In: IEEE 28th International Symposium on Software Reliability Engineering (ISSRE), 2017, IEEE p.216-227.
  29. MELLUZZI NETO, G. et al. Resultados da implantação de CMMI e MPS-BR em empresas de desenvolvimento e manutenção de software: a visão da alta gestão [Results of the implementation of CMMI and MPS-BR in software development and maintenance companies: the view of top management]. Revista Brasileira de Computação Aplicada, v.10, n.1, p.2-10, 2018.
  30. MHAWISH, M.Y.; GUPTA, M. Software Metrics and tree-based machine learning algorithms for distinguishing and detecting similar structure design patterns. SN Applied Sciences, v.2, n.1, p.11, 2020.
  31. MISIRLIS, N.; VLACHOPOULOU, M. Social media metrics and analytics in marketing – S3M: A mapping literature review. International Journal of Information Management, v.38, n.1, p.270-276, 2018.
  32. PADMINI, K.V.J.; BANDARA, H.M.N.D.; PERERA, I. Use of software metrics in agile software development process. In: Moratuwa Engineering Research Conference (MERCon). IEEE, 2015. p.312-317.
  33. PETKOV, A. A software metric for the evaluation of testing efficiency. In: AIP Conference Proceedings. AIP Publishing, 2016, p.060001.
  34. PRESSMAN, R.; MAXIM, B. Engenharia de Software. 8ª Edição. McGraw Hill Brasil, 2016.
  35. RADJENOVIĆ, D. et al. Software fault prediction metrics: A systematic literature review. Information and software technology, v.55, n.8, p.1397-1418, 2013.
  36. RIZVI, S.W.A.; SINGH, V.K.; KHAN, R.A. The state of the art in software reliability prediction: software metrics and fuzzy logic perspective. In: Information Systems Design and Intelligent Applications. Springer, New Delhi, 2016, p.629-637.
  37. SHEETAL, A.P.; RAVINDRANATH, K. Software metric evaluation on cloud based applications. In: International Journal of Engineering & Technology, 2018, v.7, p.13-18.
  38. SILVA, G.B.; COSTA, H.G.; BARROS, M.D. Entrepreneurship in engineering education: A literature review. International Journal of Engineering Education, v. 31, n.6A, p.1701-1710, 2015.
  39. SOFTEX. (Brazilian Software Excellence Promotion Association). MPS.BR– Brazilian Software Process Improvement: General MPS Software Guide, 2016.
  40. SOMMERVILLE, I. Engenharia de software [Software engineering]. 10ªEd. Pearson Prentice Hall, 2019.
  41. TEMPERO, E.; RALPH, P. A framework for defining coupling metrics. Science of Computer Programming, v.166, n.15, p.214-230, 2018.
  42. TRIPATHI, M.K. et al. Prediction of Quality of Service Parameters Using Aggregate Software Metrics and Machine Learning Techniques. In: 2018 15th IEEE India Council International Conference (INDICON). IEEE, 2018. p.1-6.
  43. TUMMALAPALLI, S.; KUMAR, L.; MURTHY, N.L.B. Prediction of Web Service Anti-patterns: Using Aggregate Software Metrics and Machine Learning Techniques. In: Proceedings of the 13th Innovations in Software Engineering Conference on Formerly known as India Software Engineering Conference, 2020. p.1-11.
  44. VOGEL, R.; GÜTTEL, W.H. The Dynamic Capability View in Strategic Management: A Bibliometric Review. International Journal of Management Reviews, v.15, p.426-446, 2013.
  45. WAZLAWICK, R. Engenharia de software: conceitos e práticas [Software engineering: concepts and practices]. Elsevier Editora Ltda., 2019.
  46. ZHANG, F.; WU, S. Predicting future influence of papers, researchers, and venues in a dynamic academic network. Journal of Informetrics, v.14, n.2, p.101035-101059, 2020.
  47. ZHU, J.; LIU, W. A tale of two databases: the use of Web of Science and Scopus in academic papers. Scientometrics, p.1-15, 2020.