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The performance improvement of the Dichotomic algorithm over the basic TC impact prediction. One point represents a mutation operator for a given project and is located at coordinates (x = FT C , y = F Dichotomic ).
Source publication
Change impact analysis consists in predicting the impact of a code change in
a software application. In this paper, we take a learning perspective on change
impact analysis and consider the problem formulated as follows. The artifacts
that are considered are methods of object-oriented software; the change under
study is a change in the code of the...
Context in source publication
Citations
... The accuracy of the impact estimation has two points: whether the predicted impacted items are actually impacted and whether all of the actually impacted items are predicted. Accordingly, learning algorithms have been studied to improve impact estimation based on call graphs [11]. Finally, open-source software projects and determined mutation operators have been studied on the estimation of effect propagation. ...
As the complexity of modern software systems increases, changes in software have become crucial to the software lifecycle. For this reason, it is an important issue for software developers to analyze the changes that occur in the software and to prevent the changes from causing errors in the software. In this paper, mutation testing as software test analysis is examined. Mutation tests have been implemented on open-source Java projects. In addition, for aviation projects, emphasis is placed on performing change impact analysis processes in compliance with the certification based on DO-178C processes.
In this paper, we propose an architecture model called Design Rule Space (DRSpace). We model the architecture of a software system as multiple overlapping DRSpaces, reflecting the fact that any complex software system must contain multiple aspects, features, patterns, etc. We show that this model provides new ways to analyze software quality. In particular, we introduce an Architecture Root detection algorithm that captures DRSpaces containing large numbers of a project's bug-prone files, which are called Architecture Roots (ArchRoots). After investigating ArchRoots calculated from 15 open source projects, the following observations become clear: from 35% to 91% of a project's most bug-prone files can be captured by just 5 ArchRoots, meaning that bug-prone files are likely to be architecturally connected. Furthermore, these ArchRoots tend to live in the system for significant periods of time, serving as the major source of bug-proneness and high maintainability costs. Moreover, each ArchRoot reveals multiple architectural flaws that propagate bugs among files and this will incur high maintenance costs over time. The implication of our study is that the quality, in terms of bug-proneness, of a large, complex software project cannot be fundamentally improved without first fixing its architectural flaws.
