A common framework for aspect mining based on crosscutting concern sorts

TL;DR

This paper proposes a unified framework based on crosscutting concern sorts for comparing, assessing, and combining aspect mining techniques to improve their effectiveness and consistency.

Contribution

It introduces a common framework for aspect mining based on concern sorts, enabling standardized assessment and combination of techniques, demonstrated through implementation and benchmarking.

Findings

Framework allows consistent assessment of aspect mining techniques

Retrofitted existing technique to the new framework successfully

Combined techniques improve quality of aspect detection

Abstract

The increasing number of aspect mining techniques proposed in literature calls for a methodological way of comparing and combining them in order to assess, and improve on, their quality. This paper addresses this situation by proposing a common framework based on crosscutting concern sorts which allows for consistent assessment, comparison and combination of aspect mining techniques. The framework identifies a set of requirements that ensure homogeneity in formulating the mining goals, presenting the results and assessing their quality. We demonstrate feasibility of the approach by retrofitting an existing aspect mining technique to the framework, and by using it to design and implement two new mining techniques. We apply the three techniques to a known aspect mining benchmark and show how they can be consistently assessed and combined to increase the quality of the results. The techniques and combinations are implemented in FINT, our publicly available free aspect mining tool.