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... . 5 shows the box plots with the performance results in terms of recall, precision and F-measure of Baseline R and HaFF R, which use the reformulation operation. The upper part of Table 4 shows the mean values and standard deviations of recall, precision and F-measure. In all performance measures, HaFF R outperforms Baseline R. HaFF R ...
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In industry, software projects might span over decades and many engineers join or leave the company over time. For these reasons, no single engineer has all of the knowledge when maintenance tasks such as Traceability Link Recovery (TLR), Bug Localization (BL), and Feature Location (FL) are performed. Thus, collaboration has the potential to boost the quality of maintenance tasks since the solution of an engineer might be enhanced with contributions of solutions from other engineers. However, assembling a team of software engineers to collaborate may not be as intuitive as we might think. In the context of a worldwide industrial supplier of railway solutions, this work evaluates how the quality of TLR, BL, and FL is affected by the criteria for selecting engineers for collaboration. The criteria for collaboration are based on engineers’ profile information to select the set of search queries that are involved in the maintenance task. Collaboration is achieved by applying automatic query reformulation, and the location relies on an evolutionary algorithm. Our work uncovers how software engineers who might be seen as not being relevant in the collaboration can lead to significantly better results. A focus group confirmed the relevance of the findings.
