TextRank Based Search Term Identification for Software Change Tasks
This addresses a specific bottleneck for software developers in maintenance tasks, offering an incremental improvement over existing methods.
The paper tackles the problem of software developers struggling to identify effective search terms for implementing change requests by proposing a TextRank-based technique that automatically suggests search terms from task descriptions. Experiments on 349 change tasks show the technique is highly promising in terms of accuracy, mean average precision, and recall.
During maintenance, software developers deal with a number of software change requests. Each of those requests is generally written using natural language texts, and it involves one or more domain related concepts. A developer needs to map those concepts to exact source code locations within the project in order to implement the requested change. This mapping generally starts with a search within the project that requires one or more suitable search terms. Studies suggest that the developers often perform poorly in coming up with good search terms for a change task. In this paper, we propose and evaluate a novel TextRank-based technique that automatically identifies and suggests search terms for a software change task by analyzing its task description. Experiments with 349 change tasks from two subject systems and comparison with one of the latest and closely related state-of-the-art approaches show that our technique is highly promising in terms of suggestion accuracy, mean average precision and recall.