Code smells can be detected using tools such as a static analyzer that detects code smells based on source code metrics. Developers perform refactoring activities based on the result of such detection tools to improve source code quality. However, such an approach can be considered as reactive refactoring, i.e., developers react to code smells after they occur. This means that developers first suffer the effects of low-quality source code before they start solving code smells. In this study, we focus on proactive refactoring, i.e., refactoring source code before it becomes smelly. This approach would allow developers to maintain source code quality without having to suffer the impact of code smells. To support the proactive refactoring process, we propose a technique to detect decaying modules, which are non-smelly modules that are about to become smelly. We present empirical studies on open source projects with the aim of studying the characteristics of decaying modules. Additionally, to facilitate developers in the refactoring planning process, we perform a study on using a machine learning technique to predict decaying modules and report a factor that contributes most to the performance of the model under consideration.

Code smells are indicators of design flaws or problems in the source code. Various tools and techniques have been proposed for detecting code smells. These tools generally detect a large number of code smells, so approaches have also been developed for prioritizing and filtering code smells. However, lack of empirical data detailing how developers filter and prioritize code smells hinders improvements to these approaches. In this study, we investigated ten professional developers to determine the factors they use for filtering and prioritizing code smells in an open source project under the condition that they complete a list of five tasks. In total, we obtained 69 responses for code smell filtration and 50 responses for code smell prioritization from the ten professional developers. We found that Task relevance and Smell severity were most commonly considered during code smell filtration, while Module importance and Task relevance were employed most often for code smell prioritization. These results may facilitate further research into code smell detection, prioritization, and filtration to better focus on the actual needs of developers.

Utilizing software architecture patterns is important for reducing maintenance costs. However, maintaining code according to the constraints defined by the architecture patterns is time-consuming work. As described herein, we propose a technique to detect code fragments that are incompliant to the architecture as fine-grained architectural violations. For this technique, the dependence graph among code fragments extracted from the source code and the inference rules according to the architecture are the inputs. A set of candidate components to which a code fragment can be affiliated is attached to each node of the graph and is updated step-by-step. The inference rules express the components' responsibilities and dependency constraints. They remove candidate components of each node that do not satisfy the constraints from the current estimated state of the surrounding code fragment. If the inferred role of a code fragment does not include the component that the code fragment currently belongs to, then it is detected as a violation. We have implemented our technique for the Model-View-Controller for Web Application architecture pattern. By applying the technique to web applications implemented using Play Framework, we obtained accurate detection results. We also investigated how much does each inference rule contribute to the detection of violations.