A Developer Centered Bug Prediction Model

Dario Di Nucci, Fabio Palomba, Giuseppe De Rosa, Gabriele Bavota, Rocco Oliveto, Andrea De Lucia

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Several techniques have been proposed to accurately predict software defects. These techniques generally exploit characteristics of the code artefacts (e.g., size, complexity, etc.) and/or of the process adopted during their development and maintenance (e.g., the number of developers working on a component) to spot out components likely containing bugs. While these bug prediction models achieve good levels of accuracy, they mostly ignore the major role played by human-related factors in the introduction of bugs. Previous studies have demonstrated that focused developers are less prone to introduce defects than non-focused developers. According to this observation, software components changed by focused developers should also be less error prone than components changed by less focused developers. We capture this observation by measuring the scattering of changes performed by developers working on a component and use this information to build a bug prediction model. Such a model has been evaluated on 26 systems and compared with four competitive techniques. The achieved results show the superiority of our model, and its high complementarity with respect to predictors commonly used in the literature. Based on this result, we also show the results of a 'hybrid' prediction model combining our predictors with the existing ones.
Original languageEnglish
JournalIEEE Transactions on Software Engineering
DOIs
Publication statusPublished - 1 Jan 2018

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