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The accuracy of the SZZ algorithm is pivotal for just-in-time defect prediction because most prior studies have used the SZZ algorithm to detect defect-inducing commits to construct and evaluate their defect prediction models. The SZZ algorithm has two phases to detect defect-inducing commits: (1) linking issue reports in an issue-tracking system to possible defect-fixing commits in a version control system by using an issue-link algorithm (ILA); and (2) tracing the modifications of defect-fixing commits back to possible defect-inducing commits. Researchers and practitioners can address the second phase by using existing solutions such as a tool called . In contrast, although various ILAs have been proposed for the first phase, no large-scale studies exist in which such ILAs are evaluated under the same experimental conditions. Hence, we still have no conclusions regarding the best-performing ILA for the first phase. In this paper, we compare 10 ILAs collected from our systematic literature study with regards to the accuracy of detecting defect-fixing commits. In addition, we compare the defect prediction performance of ILAs and their combinations that can detect defect-fixing commits accurately. We conducted experiments on five open-source software projects. We found that all ILAs and their combinations prevented the defect prediction model from being affected by missing defect-fixing commits. In particular, the combination of a natural language text similarity approach, Phantom heuristics, a random forest approach, and a support vector machine approach is the best way to statistically significantly reduced the absolute differences from the ground-truth defect prediction performance. We summarized the guidelines to use ILAs as our recommendations.

Reusing open source software (OSS) components for one's own software products has become common in the modern software development. Automated license identification tools have been proposed to help developers identify OSS licenses, since a large number of licenses sometimes must be checked before attempting to reuse. Of the existing tools, Ninka [1] can most correctly identify licenses of each source file by using regular expressions. In case Ninka does not have license identification rules for unknown licenses, Ninka reports these as "unknown licenses" which must be checked by developers manually. Since completely-new or derived OSS licenses appear nearly every year, a license identification tool should be appropriately maintained by adding regular expressions corresponding to the new licenses. The final goal of our study is to construct a method to automatically create candidate license rules to be added to a license identification tool such as Ninka. Toward achieving the goal, files identified as unknown licenses must be classified by license firstly. In this paper, we propose a hierarchical clustering which divides unknown licenses into clusters of files with a single license. We conduct a case study to confirm the usefulness of our clustering method when it is applied for classifying 2,801, 1,230 and 2,446 unknown license statement files for Linux Kernel v4.4.6, FreeBSD v10.3.0 and Debian v7.8.0 respectively. As a result, it is confirmed that our method can create clusters which are suitable as candidates for generating license rules automatically.

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