A Review of Machine Learning Techniques for Software Quality Prediction

Cowlessur, Sanjeev K.; Pattnaik, Saumendra; Pattanayak, Binod Kumar

doi:10.1007/978-981-15-1483-8_45

Cited by 6 publications

(1 citation statement)

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“…Consequently, there have been multiple attempts to synthesise knowledge gained in software quality research; however, they were focused mainly on single aspects of software quality, such as measurement [5], human-software interaction [6], empirical analysis of Code smells and refactoring [7], design patterns [8]; quality models [9], human factors [10], testing [11] and quality prediction [12,13] or to specific development approaches such as agile [14].…”

Section: Introductionmentioning

confidence: 99%

Software Quality: How Much Does It Matter?

Kokol

2022

Electronics

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Interconnected computers and software systems have become an indispensable part of people’s lives in the period of digital transformation. Consequently, software quality research is becoming more and more critical. There have been multiple attempts to synthesise knowledge gained in software quality research; however, they were focused mainly on single aspects of software quality and did not structure the knowledge holistically. To fill this gap, we harvested software quality publications indexed in the Scopus bibliographic database. We analysed them using synthetic content analysis which is a triangulation of bibliometrics and content analysis. The search resulted in 15,468 publications. The performance bibliometric analysis showed that the production of research publications relating to software quality is currently following an exponential growth trend and that the software quality research community is growing. The most productive country was the United States, followed by China. The synthetic content analysis revealed that the published knowledge could be structured into six themes, the most important being the themes regarding software quality improvement by enhancing software engineering, advanced software testing and improved defect and fault prediction with machine learning and data mining.

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