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Journal-first Papers
Wed 13 Sep 2023 16:30 - 16:42 at Room D - Bug Detection Chair(s): Andreea VescanContext Advances in defect prediction models, aka classifiers, have been validated via accuracy metrics. Effort-aware metrics (EAMs) relate to benefits provided by a classifier in accurately ranking defective entities such as classes or methods. PofB is an EAM that relates to a user that follows a ranking of the probability that an entity is defective, provided by the classifier. Despite the importance of EAMs, there is no study investigating EAMs trends and validity. Aim Theaimofthispaperistwofold:1)werevealissuesinEAMsusage,and2)wepropose and evaluate a normalization of PofBs (aka NPofBs), which is based on ranking defective entities by predicted defect density. Method We perform a systematic mapping study featuring 152 primary studies in major journals and an empirical study featuring 10 EAMs, 10 classifiers, two industrial, and 12 open-source projects. Results Our systematic mapping study reveals that most studies using EAMs use only a single EAM (e.g., PofB20) and that some studies mismatched EAMs names. The main result of our empirical study is that NPofBs are statistically and by orders of magnitude higher than PofBs. Conclusions In conclusion, the proposed normalization of PofBs: (i) increases the realism of results as it relates to a better use of classifiers, and (ii) promotes the practical adoption of prediction models in industry as it shows higher benefits. Finally, we provide a tool to compute EAMs to support researchers in avoiding past issues in using EAMs. Keywords Defect prediction · Accuracy metrics · Effort-aware metrics
DOI File AttachedJournal-first Papers
Wed 13 Sep 2023 11:42 - 11:54 at Plenary Room 2 - Code Quality and Code Smells Chair(s): Bernd FischerDefect prediction can help at prioritizing testing tasks by, for instance, ranking a list of items (methods and classes) according to their likelihood to be defective. While many studies investigated how to predict the defectiveness of commits, methods, or classes separately, no study investigated how these predictions differ or benefit each other. Specifically, at the end of a release, before the code is shipped to production, testing can be aided by ranking methods or classes, and we do not know which of the two approaches is more accurate. Moreover, every commit touches one or more methods in one or more classes; hence, the likelihood of a method and a class being defective can be associated with the likelihood of the touching commits being defective. Thus, it is reasonable to assume that the accuracy of methods-defectiveness-predictions (MDP) and the class-defectiveness-predictions (CDP) are increased by leveraging commits-defectiveness-predictions (aka JIT).
The contribution of this paper is fourfold: (i) We compare methods and classes in terms of defectiveness and (ii) of accuracy in defectiveness prediction, (iii) we propose and evaluate a first and simple approach that leverages JIT to increase MDP accuracy and (iv) CDP accuracy.
We analyse accuracy using two types of metrics (threshold-independent and effort-aware). We also use feature selection metrics, nine machine learning defect prediction classifiers, more than 2.000 defects related to 38 releases of nine open source projects from the Apache ecosystem. Our results are based on a ground truth with a total of 285,139 data points and 46 features among commits, methods and classes.
Our results show that leveraging JIT by using a simple median approach increases the accuracy of MDP by an average of 17% AUC and 46% PofB10 while it increases the accuracy of CDP by an average of 31% AUC and 38% PofB20.
From a practitioner’s perspective, it is better to predict and rank defective methods than defective classes. From a researcher’s perspective, there is a high potential for leveraging statement-defectiveness-prediction (SDP) to aid MDP and CDP.
Link to publication DOI File Attached