EASE: An effort-aware extension of unsupervised key class identification approaches
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Published:2023-12-02
Issue:
Volume:
Page:
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ISSN:1049-331X
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Container-title:ACM Transactions on Software Engineering and Methodology
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language:en
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Short-container-title:ACM Trans. Softw. Eng. Methodol.
Author:
Pan Weifeng1, Kessentini Marouane2, Ming Hua3, Yang Zijiang4
Affiliation:
1. School of Computer Science and Technology, Zhejiang Gongshang University, China 2. College of Innovation and Technology, University of Michigan-Flint, USA 3. School of Engineering and Computer Science, Oakland University, USA 4. School of Computer Science, Xi’an Jiaotong University, China
Abstract
Key class identification approaches aim at identifying the most important classes to help developers, especially newcomers, start the software comprehension process. So far, many supervised and unsupervised approaches have been proposed; however, they have not considered the effort to comprehend classes. In this paper, we identify the challenge of “
effort-aware key class identification
”; to partially tackle it, we propose an approach,
EASE
, which is implemented through a modification to existing unsupervised key class identification approaches to take into consideration the effort to comprehend classes. First,
EASE
chooses a set of network metrics that have a wide range of applications in the existing unsupervised approaches and also possess good
discriminatory power
. Second,
EASE
normalizes the network metric values of classes to quantify the probability of any class to be a key class, and utilizes
Cognitive Complexity
to estimate the effort required to comprehend classes. Third,
EASE
proposes a metric,
RKCP
, to measure the
relative key-class proneness
of classes and further uses it to sort classes in descending order. Finally, an effort threshold is utilized, and the top-ranked classes within the threshold are identified as the cost-effective key classes. Empirical results on a set of eighteen software systems show that i) the proposed effort-aware variants perform significantly better in almost all (≈ 98.33%) the cases, ii) they are superior to most of the baseline approaches with only several exceptions, and iii) they are scalable to large-scale software systems. Based on these findings, we suggest that i) we should resort to effort-aware key class identification techniques in budget-limited scenarios; and ii) when using different techniques, we should carefully choose the weighting mechanism so as to obtain the best performance.
Publisher
Association for Computing Machinery (ACM)
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