An empirical study on the interplay between semantic coupling and co-change of software classes

Abstract

Software systems continuously evolve to accommodate new features and interoperability relationships between artifacts point to increasingly relevant software change impacts. During maintenance, developers must ensure that related entities are updated to be consistent with these changes. Studies in the static change impact analysis domain have identified that a combination of source code and lexical information outperforms using each one when adopted independently. However, the extraction of lexical information and the measure of how loosely or closely related two software artifacts are, considering the semantic information embedded in their comments and identifiers has been carried out using somewhat complex information retrieval (IR) techniques. The interplay between software semantic and change relationship strengths has also not been extensively studied. This work aims to fill both gaps by comparing the effectiveness of measuring semantic coupling of OO software classes using (i) simple identifier based techniques and (ii) the word corpora of the entire classes in a software system. Afterwards, we empirically investigate the interplay between semantic and change coupling. The empirical results show that: (1) identifier based methods have more computational efficiency but cannot always be used interchangeably with corpora-based methods of computing semantic coupling of classes and (2) there is no correlation between semantic and change coupling. Furthermore we found that (3) there is a directional relationship between the two, as over 70% of the semantic dependencies are also linked by change coupling but not vice versa.

Bab (Business Application Builder) Framework for Rapid Development of Business Information Systems

“…Apart from the above-mentioned issues that are obvious and require short-term resolution, there is plenty of space for substantial improvement of the BAB Framework. Meta modeling enhanced with appropriate AI techniques (i.e., semantic technologies for semantic coupling and co-change of software components [21], explainable AI for defect prediction models [22], large language models [23] for automated models and/or code generation, and alike) is the road that should be hit in the future to improve substantially of the BAB Framework.…”

DOOR: Distributed Object Oriented Software Restructuring Approach Using Neural Network

“…5 обобщаются результаты, описанные в статье. [17] была сделана попытка связать эффективность измерения семантического связывания (Semantic Coupling, SC) классов объектно-ориентированных программ, используя 1) простые методы, ориентированные на идентификаторы, и 2) полные корпуса классов в программной системе. Впоследствии они исследовали взаимодействие между семантическим и изменяемым связыванием (Change Coupling, CC).…”