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2015

Aggarwal, K., Rutgers, T., Timbers, F., Hindle, Abram, Greiner, R., Stroulia, E.

In previous work by Alipour et al., a methodology was proposed for detecting duplicate bug reports by comparing the textual content of bug reports to subject-specific contextual material, namely lists of software-engineering terms, such as non-functional requirements and architecture keywords. When

-engineering literature. Evaluating this software-literature context method on real-world bug reports produces useful results that indicate this semi-automated method has the potential to substantially decrease the manual effort used in contextual bug deduplication while suffering only a minor loss in accuracy.

a bug report contains a word in these word-list contexts, the bug report is considered to be associated with that context and this information tends to improve bug-deduplication methods. In this paper, we propose a method to partially automate the extraction of contextual word lists from software

2017

Aggarwal, K., Timbers, F., Rutgers, T., Hindle, Abram, Stroulia, E., Greiner, R.

Bug deduplication, ie, recognizing bug reports that refer to the same problem, is a challenging task in the software-engineering life cycle. Researchers have proposed several methods primarily relying on information-retrieval techniques. Our work motivated by the intuition that domain knowledge can

provide the relevant context to enhance effectiveness, attempts to improve the use of information retrieval by augmenting with software-engineering knowledge. In our previous work, we proposed the software-literature-context method for using software-engineering literature as a source of contextual

information to detect duplicates. If bug reports relate to similar subjects, they have a better chance of being duplicates. Our method, being largely automated, has a potential to substantially decrease the level of manual effort involved in conventional techniques with a minor trade-off in accuracy. In this