Integrating conceptual and logical couplings for change impact analysis in software

Abstract

The paper presents an approach that combines conceptual and evolutionary techniques to support change impact analysis in source code. Conceptual couplings capture the extent to which domain concepts and software artifacts are related to each other. This information is derived using Information Retrieval based analysis of textual software artifacts that are found in a single version of software (e.g., comments and identifiers in a single snapshot of source code). Evolutionary couplings capture the extent to which software artifacts were co-changed. This information is derived from analyzing patterns, relationships, and relevant information of source code changes mined from multiple versions in software repositories. The premise is that such combined methods provide improvements to the accuracy of impact sets compared to the two individual approaches.A rigorous empirical assessment on the changes of the open source systems Apache httpd, ArgoUML, iBatis, KOffice, and jEdit is also reported. The impact sets are evaluated at the file and method levels of granularity for all the software systems considered in the empirical evaluation.The results show that a combination of conceptual and evolutionary techniques, across several cutoff points and periods of history, provides statistically significant improvements in accuracy over either of the two techniques used independently. Improvements in F-measure values of up to 14%(from 3% to 17%) over the conceptual technique in ArgoUML at the method granularity, and up to 21% over the evolutionary technique in iBatis (from 9% to 30%) at the file granularity were reported.

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

“…For example, the rule {A, B}→ C found in the sales data of a supermarket indicates that a customer who buys A and B together, is also likely to buy C (Oliva and Gerosa 2011). Two classes change at the same time when changes in one class A are made in response to a change in another class B. Kagdi et al (2013) state that logical coupling captures the extent to which software artefacts co-evolve and this information is derived by analysing patterns, relationships and relevant information of source code changes mined from multiple versions (of software systems) in software repositories (e.g., Subversion and Bugzilla). According to Lanza et al (D'Ambros et al 2006) it is useful to study logical coupling because it can reveal dependencies not revealed by analyzing the source code (Yu 2007) only.…”

“…Identifiers used by developers for names of classes, methods, or attributes in source code or other artifacts contain important information and account for approximately half of the source code in software (Kagdi et al 2013). These names often serve as a starting point in many program comprehension tasks.…”

“…These names often serve as a starting point in many program comprehension tasks. Hence, it is essential that these names clearly reflect the concepts that they are supposed to represent, as self-documenting identifiers decrease the time and effort needed to acquire a basic comprehension level for a programming task (Kagdi et al 2013). Figure 2 shows a simplified scenario that underlies our motivation: previous research (Yu 2007) (pictured inside the grey box) has shown that the structural coupling between classes causes them to be co-changed, and it plays an important role in the measurement of coevolution (Yu 2007).…”

“…However, the frequency of change couplings have not yet been studied in relation to semantic coupling. The computation of semantic coupling in studies in this domain have been done by using information retrieval techniques such as latent semantic indexing (LSI) and vector space modelling (VSM) (Poshyvanyk and Marcus 2006;Poshyvanyk et al 2009;Kagdi et al 2013) to analyze the corpora of OO software classes (after transforming the semantic information from source code into a text or words corpus).…”

“…Minimizing historical data extraction and analysis efforts: semantic coupling metrics will be used to inform or predict the strength of the logical dependencies between classes without the need to analyze historical data of software projects, thus reducing the effort required (i.e., computation time and data storage) in the detection of logical dependencies via mining software repositories. The semantic similarity between class identifiers will also be used in the ranking of classes that might be impacted by a given change request without having to analyze software evolution or historical data, thus minimizing the effort required in change impact analysis (Kagdi et al 2013). …”