Applications of Multi-view Learning Approaches for Software Comprehension

TL;DR

This paper explores multi-view learning techniques to enhance software comprehension by integrating diverse data sources, improving modularization, refactoring recommendations, and cross-view source code search in large Java systems.

Contribution

It introduces novel multi-view learning methods tailored for software comprehension, demonstrating their effectiveness in modularization, refactoring, and cross-view retrieval tasks.

Findings

Multi-view fusion guarantees a lower bound on modularization quality.

Joint subspace learning improves cross-view source code search.

Multi-view approaches can be applied to other software engineering problems.

Abstract

Program comprehension concerns the ability of an individual to make an understanding of an existing software system to extend or transform it. Software systems comprise of data that are noisy and missing, which makes program understanding even more difficult. A software system consists of various views including the module dependency graph, execution logs, evolutionary information and the vocabulary used in the source code, that collectively defines the software system. Each of these views contain unique and complementary information; together which can more accurately describe the data. In this paper, we investigate various techniques for combining different sources of information to improve the performance of a program comprehension task. We employ state-of-the-art techniques from learning to 1) find a suitable similarity function for each view, and 2) compare different multi-view learning techniques to decompose a software system into high-level units and give component-level recommendations for refactoring of the system, as well as cross-view source code search. The experiments conducted on 10 relatively large Java software systems show that by fusing knowledge from different views, we can guarantee a lower bound on the quality of the modularization and even improve upon it. We proceed by integrating different sources of information to give a set of high-level recommendations as to how to refactor the software system. Furthermore, we demonstrate how learning a joint subspace allows for performing cross-modal retrieval across views, yielding results that are more aligned with what the user intends by the query. The multi-view approaches outlined in this paper can be employed for addressing problems in software engineering that can be encoded in terms of a learning problem, such as software bug prediction and feature location.