Can we predict types of code changes? An empirical analysis

Abstract

Abstract-There exist many approaches that help in pointing developers to the change-prone parts of a software system. Although beneficial, they mostly fall short in providing details of these changes. Fine-grained source code changes (SCC) capture such detailed code changes and their semantics on the statement level. These SCC can be condition changes, interface modifications, inserts or deletions of methods and attributes, or other kinds of statement changes. In this paper, we explore prediction models for whether a source file will be affected by a certain type of SCC. These predictions are computed on the static source code dependency graph and use social network centrality measures and object-oriented metrics. For that, we use change data of the Eclipse platform and the Azureus 3 project. The results show that Neural Network models can predict categories of SCC types. Furthermore, our models can output a list of the potentially change-prone files ranked according to their change-proneness, overall and per change type category.

Analysing the Linux kernel feature model changes using FMDiff

“…We found a minor modification to value attribute (long integer allowed on value based features for instance). 13 We also found modifications to the allowed values on feature attribute "option" 14 as mentioned in Sect. 2, irrelevant in the context of this study.…”

“…We can find in the literature accounts of the issues arising during the evolution of such systems [1,19,42]. In a different domain, it has been shown that the analysis of fine-grained source code changes facilitates software maintenance [14]. Encouraged by such results, we propose to explore a similar idea in the context of highly variable software: observing the details of the fine-grained evolution of a feature model to derive information about the evolution of the system.…”

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