PROJECT TITLE :

Replicating and Re-Evaluating the Theory of Relative Defect-Proneness

ABSTRACT:

A smart understanding of the factors impacting defects in software systems is crucial for software practitioners, as a result of it helps them prioritize quality improvement efforts (e.g., testing and code reviews). Defect prediction models are typically designed using classification or regression analysis on product and/or process metrics collected at a single purpose in time (e.g., a unleash date). However, current defect prediction models solely predict if a defect will occur, however not when, that makes the prioritization of software quality improvements efforts tough. To deal with this downside, Koru et al. applied survival analysis techniques to a massive variety of software systems to study how size (i.e., lines of code) influences the chance that a source code module (e.g., class or file) will expertise a defect at any given time. Given that 1) the work of Koru et al. has been instrumental to our understanding of the scale-defect relationship, 2) the utilization of survival analysis within the context of defect modelling has not been well studied and 3) replication studies are an important part of balanced scholarly discussion, we have a tendency to gift a replication study of the work by Koru et al. In explicit, we tend to present the main points necessary to use survival analysis in the context of defect modelling (such details were missing from the initial paper by Koru et al.). We tend to conjointly explore how variations between the traditional domains of survival analysis (i.e., medication and epidemiology) and defect modelling impact our understanding of the scale-defect relationship. Practitioners and researchers considering the use of survival analysis should bear in mind of the implications of our findings.