TextRank Based Search Term Identification for Software Change Tasks

TL;DR

This paper introduces a TextRank-based method to automatically identify effective search terms from natural language change requests, improving software maintenance by aiding developers in locating relevant code segments.

Contribution

The paper presents a novel TextRank-based approach for automatic search term suggestion in software change tasks, outperforming existing methods in accuracy and recall.

Findings

High suggestion accuracy demonstrated in experiments

Significant improvement over state-of-the-art approaches

Effective in real-world software maintenance scenarios

Abstract

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.