Using Consensual Biterms from Text Structures of Requirements and Code to Improve IR-Based Traceability Recovery

TL;DR

This paper introduces a novel method using consensual biterms from text structures of requirements and code to enhance IR-based traceability recovery, significantly improving the accuracy of automated link detection in software artifacts.

Contribution

It proposes a new approach to extract and filter co-occurring word pairs from requirements and code texts to improve IR-based traceability recovery performance.

Findings

Outperforms baseline by 21.9% in AP and 9.3% in MAP when used alone.

Enhances IR techniques by enriching input corpus and IR calculations.

Collaborates effectively with other strategies, outperforming baselines by 5.9% in AP.

Abstract

Traceability approves trace links among software artifacts based on whether two artifacts are related by system functionalities. The traces are valuable for software development, but are difficult to obtain manually. To cope with the costly and fallible manual recovery, automated approaches are proposed to recover traces through textual similarities among software artifacts, such as those based on Information Retrieval (IR). However, the low quality & quantity of artifact texts negatively impact the calculated IR values, thus greatly hindering the performance of IR-based approaches. In this study, we propose to extract co-occurred word pairs from the text structures of both requirements and code (i.e., consensual biterms) to improve IR-based traceability recovery. We first collect a set of biterms based on the part-of-speech of requirement texts, and then filter them through the code texts. We then use these consensual biterms to both enrich the input corpus for IR techniques and enhance the calculations of IR values. A nine-system-based evaluation shows that in general, when solely used to enhance IR techniques, our approach can outperform pure IR-based approaches and another baseline by 21.9% & 21.8% in AP, and 9.3% & 7.2% in MAP, respectively. Moreover, when used to collaborate with another enhancing strategy from different perspectives, it can outperform this baseline by 5.9% in AP and 4.8% in MAP.