Ranking of software developers based on expertise score for bug triaging

Abstract Context Existing bug triage approaches for developer recommendation systems are mainly based on machine learning (ML) techniques. These approaches have shown low prediction accuracy and high bug tossing length (BTL). Objective The objective of this paper is to develop a robust algorithm for reducing BTL based on the concept of developer expertise score (DES). Method None of the existing approaches to the best of our knowledge have utilized metrics to build developer expertise score. The novel strategy of DES is consisted of two stages: Stage-I consisted of an offline process for detecting the developers based on DES which computes the score using priority, versatility and average fix-time for his individual contributions. The online system process consisted of finding the capable developers using three kinds of similarity measures (feature-based, cosine-similarity and Jaccard). Stage-II of the online process consisted of simply ranking the developers. Hit-ratio and reassignment accuracy were used for performance evaluation. We compared our system against the ML-based bug triaging approaches using three types of classifiers: Navies Bayes, Support Vector Machine and C4.5 paradigms. Results By adapting the five open source databases, namely: Mozilla, Eclipse, Netbeans, Firefox, and Freedesktop, covering 41,622 bug reports, our novel DES system yielded a mean accuracy, precision, recall rate and F-score of 89.49%, 89.53%, 89.42% and 89.49%, respectively, reduced BTLs of up to 88.55%. This demonstrates an improvement of up to 20% over existing strategies. Conclusion This work presented a novel developer recommendation algorithm to rank the developers based on a metric-based integrated score for bug triaging. This integrated score was based on the developer's expertise with an objective to improve (i) bug assignment and (ii) reduce the bug tossing length. Such architecture has an application in software bug triaging frameworks.