Relating Developers’ Concepts and Artefact Vocabulary in a Financial Software Module

Developers working on unfamiliar systems are challenged to accurately identify where and how high-level concepts are implemented in the source code. Without additional help, concept location can become a tedious, time-consuming and error-prone task. In this paper we study an industrial financial application for which we had access to the user guide, the source code, and some change requests. We compared the relative importance of the domain concepts, as understood by developers, in the user manual and in the source code. We also searched the code for the concepts occurring in change requests, to see if they could point developers to code to be modified. We varied the searches (using exact and stem matching, discarding stop-words, etc.) and present the precision and recall. We discuss the implication of our results for maintenance

Software defect prediction: do different classifiers find the same defects?

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio