Methods and tools to support quality assessment and code anomaly detection are crucial to enable software evolution and maintenance. In this work, we aim to detect an increase or decrease in code anomalies leveraging on the concept of microstructures, which are relationships between entities in the code. We introduce a tools pipeline, called Cadartis, which uses an innovative immune-inspired approach for code anomaly detection, tailored to the organization's needs. This approach has been evaluated on 3882 versions of fifteen open-source projects belonging to three different organizations and the results confirm that the approach can be applied to recognize a decrease or increase of code anomalies (anomalous status). The tools pipeline has been designed to automatically learn patterns of microstructures from previous versions of existing systems belonging to the same organization, to build a personalized quality profiler based on its codebase. This work represents a first step towards new perspectives in the field of software quality assessment and it could be integrated into continuous integration pipelines to profile software quality during the development process.

The detection of performance issues in Java-based applications is not trivial since many factors concur to poor performance, and software engineers are not sufficiently supported for this task. The goal of this manuscript is the automated detection of performance problems in running systems to guarantee that no quality-based hinders prevent their successful usage. Starting from software performance antipatterns, i.e., bad practices (e.g., extensive interaction between software methods) expressing both the problem and the solution with the purpose of identifying shortcomings and promptly fixing them, we develop a framework that automatically detects seven software antipatterns capturing a variety of performance issues in Java-based applications. Our approach is applied to real-world case studies from different domains, and it captures four real-life performance issues of Hadoop and Cassandra that were not predicted by state-of-the-art approaches. As empirical evidence, we calculate the accuracy of the proposed detection rules, we show that code commits inducing and fixing real-life performance issues present interesting variations in the number of detected antipattern instances, and solving one of the detected antipatterns improves the system performance up to 50%.

Architectural smells gained great attention in the past few years since they directly affect software quality and increase architectural technical debt. However, while it is straightforward to understand why they are important, it is more difficult to find techniques and tools to detect and remove architectural smells. The purpose of this paper is to introduce an open-source tool for automatic architectural smells detection for C/C++ projects, by creating an abstraction of the project and defining the concept of dependency between elements belonging to the project in order to identify architectural smells. The tool has been validated on some open-source projects with promising results.