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45. Legion: Massively Composing Rankers for Improved Bug Localization at Adobe

Author

Riley Smith, David Lo, Darryl Jarman, Ferdian Thung, and Jeffrey Berry

Subjects
Database, Computer science, business.industry, media_common.quotation_subject, Adobe, engineering.material, computer.software_genre, Random forest, Debugging, Analytics, engineering, Mean reciprocal rank, business, computer, Software, media_common
Abstract

Studies have estimated that, in industrial settings, developers spend between 30% and 90% of their time fixing bugs. As such, tools that assist in identifying the location of bugs provide value by reducing debugging costs. One such tool is BugLocator. This study initially aimed to determine if developers working on the Adobe Analytics product could use BugLocator. The initial results show that BugLocator achieves a similar accuracy on 5 of 7 Adobe Analytics repositories and on open-source projects. However, these results do not meet the minimum applicability requirement deemed necessary by Adobe Analytics developers prior to possible adoption. Thus, we consequently examine how BugLocator can achieve the targeted accuracy with two extensions: (1) adding more data corpora, and (2) massively composing individual rankers consisting of augmented BugLocator instances trained on various combinations of corpora and parameter configurations with a Random Forest model. We refer to our final extension as Legion. On average, applying Legion to Adobe Analytics repositories results in at least one buggy file ranked in the top-10 recommendations 76.8% of the time for customer-reported bugs across all 7 repositories. This represents a substantial improvement over BugLocator of 36.4%, and satisfies the minimum applicability requirement. Additionally, our extensions boost Mean Average Precision by 107.7%, Mean Reciprocal Rank by 86.1%, Top 1 by 143.4% and Top 5 by 58.1%.

Published
2022

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