PairSmell: A Novel Perspective Inspecting Software Modular Structure
Chenxing Zhong, Daniel Feitosa, Paris Avgeriou, Huang Huang, Yue Li,, He Zhang

TL;DR
PairSmell introduces a method to identify flawed modular relations in software by comparing actual and ideal entity pairings, aiding targeted refactoring and improving software quality.
Contribution
The paper proposes a novel approach, PairSmell, combining modularization tools to detect specific architectural issues at the relation level, enhancing refactoring guidance.
Findings
14.60% and 20.44% of entities involved in PairSmell instances
InSep pairs lead to 190% more co-changes, InCol pairs fewer
PairSmell persists across software evolution
Abstract
Enhancing the modular structure of existing systems has attracted substantial research interest, focusing on two main methods: (1) software modularization and (2) identifying design issues (e.g., smells) as refactoring opportunities. However, re-modularization solutions often require extensive modifications to the original modules, and the design issues identified are generally too coarse to guide refactoring strategies. Combining the above two methods, this paper introduces a novel concept, PairSmell, which exploits modularization to pinpoint design issues necessitating refactoring. We concentrate on a granular but fundamental aspect of modularity principles -- modular relation (MR), i.e., whether a pair of entities are separated or collocated. The main assumption is that, if the actual MR of a pair violates its `apt MR', i.e., an MR agreed on by multiple modularization tools (as…
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Taxonomy
TopicsSoftware System Performance and Reliability · Software Engineering Research · Advanced Software Engineering Methodologies
