Toward a Smell-aware Bug Prediction Model

Code smells are symptoms of poor design and implementation choices. Previous studies empirically assessed the impact of smells on code quality and clearly indicate their negative impact on maintainability, including a higher bug-proneness of components affected by code smells. In this paper, we capture previous findings on bug-proneness to...

The Scent of a Smell: An Extensive Comparison between Textual and Structural Smells

Code smells are symptoms of poor design or implementation choices that have a negative effect on several aspects of software maintenance and evolution, such as program comprehension or change- and fault-proneness. This is why researchers have spent a lot of effort on devising methods that help developers to automatically detect them in source...

Automatic Test Smell Detection using Information Retrieval Techniques

Software testing is a key activity to control the reliability of production code. Unfortunately, the effectiveness of test cases can be threatened by the presence of faults. Recent work showed that static indicators can be exploited to identify testrelated issues. In particular test smells, i.e., sub-optimal design choices applied by developers...

Enhancing Change Prediction Models using Developer-Related Factors

Continuous changes applied during software maintenance risk to deteriorate the structure of a system and are a threat to its maintainability. In this context, predicting the portions of source code where specific maintenance operations should be focused on may be crucial for developers to prevent maintainability issues. Previous work proposed...

On the diffuseness and the impact on maintainability of code smells: A large scale empirical investigation

Code smells are symptoms of poor design and implementation choices that may hinder code comprehensibility and maintainability. Despite the effort devoted by the research community in studying code smells, the extent to which code smells in software systems affect software maintainability remains still unclear. In this paper we present a large...

Developer-Related Factors in Change Prediction: An Empirical Assessment

Predicting the areas of the source code having a higher likelihood to change in the future is a crucial activity to allow developers to plan preventive maintenance operations such as refactoring or peer-code reviews. In the past the research community was active in devising change prediction models based on structural metrics extracted from the...

Adaptive User Feedback for IR-Based Traceability Recovery

Trace ability recovery allows software engineers to understand the interconnections among software artefacts and, thus, it provides an important support to software maintenance activities. In the last decade, Information Retrieval (IR) has been widely adopted as core technology of semi-automatic tools to extract trace ability links between...