Abstract
While studying the reliability of software systems, the phenomenon to address the complexity of the code change process must be considered. The reasons behind this complexity in the code can either be bug removal phenomenon, new feature addition, or feature improvements, to name a few. Keeping in mind, the need to measure the complexity of the code changes, the authors have developed a modeling framework with an assumption that at any given time point, the complexity of code changes is impacted by at least any one of the above-specified attributes. To validate this developed framework, the authors have utilized certain open-source data sets and have presented their applicability using the SPSS software package. The obtained results are in line with the presented modeling framework.
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References
Goel AL, Okumoto K (1979) Time-dependent error-detection rate model for software reliability and other performance measures. IEEE Trans Reliab 28(3):206–211
Yamada S, Ohba M, Osaki S (1983) S-shaped reliability growth modeling for software error detection. IEEE Trans Reliab 32(5):475–484
Kapur PK, Garg RB (1992) A software reliability growth model for an error-removal phenomenon. Softw Eng J 7(4):291–294
Kapur PK, Goswami DN, Gupta A (2004) A software reliability growth model with testing effort dependent learning function for distributed systems. Int J Reliab Qual Saf Eng 11(04):365–377
Singh O, Kapur PK, Khatri SK, Singh JNP (2012). Software reliability growth modeling for successive releases. In: Proceeding of 4th international conference on quality, reliability and Infocom technology (ICQRIT), pp 77–87
Kumar V, Mathur P, Sahni R, Anand M (2016) Two-dimensional multi-release software reliability modeling for fault detection and fault correction processes. Int J Reliab Qual Saf Eng 23(03):1640002
Kumar V, Sahni R, Shrivastava AK (2016) Two-dimensional multi-release software modelling with testing effort, time and two types of imperfect debugging. Int J Reliab Saf 10(4):368–388
Anand A, Agarwal M, Tamura Y, Yamada S (2017) Economic impact of software patching and optimal release scheduling. Qual Reliab Eng Int 33(1):149–157
Anand A, Das S, Singh O, Kumar V (Feb 2019) Resource allocation problem for multi versions of software system. In: 2019 amity international conference on artificial intelligence (AICAI). IEEE, pp 571–576
Li Q, Pham H (2021) Software reliability modeling incorporating fault detection and fault correction processes with testing coverage and fault amount dependency. Mathematics 10(1):60
Saxena P, Ram M (2022) Two phase software reliability growth model in the presence of imperfect debugging and error generation under fuzzy paradigm. Math Eng Sci Aerosp (MESA) 13(2)
Kumar V, Pham H (eds) (2022) Predictive analytics in system reliability. Springer Nature
Singh VB, Sharma M (Nov 2014) Prediction of the complexity of code changes based on number of open bugs, new feature and feature improvement. In: 2014 IEEE international symposium on software reliability engineering workshops. IEEE, pp 478–483
Raghuvanshi KK, Sharma M, Tandon A, Singh VB (2018) Quantitative quality assessment of open source software by considering new features and feature improvements. In: Computational science and its applications–ICCSA 2018: 18th international conference, Melbourne, VIC, Australia, July 2–5, 2018, Proceedings, Part V 18. Springer International Publishing, pp 412–423
Tandon A, Sharma M, Kumari M, Singh VB (2020) Entropy based software reliability growth modelling for open source software evolution. Tehnički vjesnik 27(2):550–557
Bhatt N, Anand A, Yadavalli VSS, Kumar V (2017) Modeling and characterizing software vulnerabilities. Int J Math Eng Manag Sci 2(4):288–299
Gupta P, Anand A, Ram M (2021) Reliability as key software quality metric: a multi-criterion intuitionistic fuzzy-topsis-based analysis. Int J Reliab Qual Saf Eng 28(06):2140003
Hassan AE (May 2009) Predicting faults using the complexity of code changes. In: 2009 IEEE 31st international conference on software engineering. IEEE, pp 78–88
Hanssen G, Yamashita AF, Conradi R, Moonen L (Jan 2010) Software entropy in agile product evolution. In: 2010 43rd Hawaii international conference on system sciences. IEEE, pp 1–10
Canfora G, Cerulo L, Di Penta M, Pacilio F (Jun 2010) An exploratory study of factors influencing change entropy. In: 2010 IEEE 18th international conference on program comprehension. IEEE, pp 134–143
Chaturvedi KK, Bedi P, Misra S, Singh VB (Dec 2013) An empirical validation of the complexity of code changes and bugs in predicting the release time of open source software. In: 2013 IEEE 16th international conference on computational science and engineering. IEEE, pp 1201–1206
Chaturvedi KK, Kapur PK, Anand S, Singh VB (2014) Predicting the complexity of code changes using entropy based measures. Int J Syst Assur Eng Manag 5:155–164
Singh VB, Chaturvedi KK, Khatri SK, Kumar V (2015) Bug prediction modeling using complexity of code changes. Int J Syst Assur Eng Manag 6:44–60
Singh VB, Chaturvedi KK, Khatri S, Sharma M (2017) Complexity of the code changes and issues dependent approach to determine the release time of software product. In: Computational science and its applications–ICCSA 2017: 17th international conference, Trieste, Italy, July 3–6, 2017, Proceedings, Part V 17. Springer International Publishing, pp 519–529
Anand A, Bharmoria S, Ram M (2019) Characterizing the complexity of code changes in open source software. In: Recent advancements in software reliability assurance. CRC Press, pp 1–14
Kapur PK, Panwar S, Kumar V, Singh O (2020) Entropy-based two-dimensional software reliability growth modeling for open-source software incorporating change-point. Int J Reliab Qual Saf Eng 27(05):2040009
https://www.helpspot.com/blog/does-your-software-product-need-new-features
Singh O, Anand A, Singh J (2021) SDE based unified scheme for developing entropy prediction models for OSS. Int J Math Eng Manag Sci 6(1):207
Acknowledgements
The work done in this chapter has been supported by grants received by the third author from Institute of Eminence, DU, India as part of Faculty Research Program via Ref. no. /IOE/2021/12/FRP.
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Yadav, A., Singh, O., Anand, A., Verma, R., Singh, I. (2024). Exploring Software Systems Engineering Through Complexity of Code Changes: A Study Based on Bugs Count, Features Improvement and New Add-Ons. In: Kapur, P.K., Pham, H., Singh, G., Kumar, V. (eds) Reliability Engineering for Industrial Processes. Springer Series in Reliability Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-55048-5_5
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