Framework for Automated Code Smell Correction in a BrownVeld Context

Developing code over a long period of time, in a heterogeneous team or under time pressure can lead to a lingering decay in readability, maintainability and thus reusability of software source code. In the past decades several approaches have been developed that tackle automatic detection of such code Waws, yet neglecting the automated correction as an essential and integrated step in the refactoring workWow. Developers rather had to refactor identiVed code Waws manually which is often a time-consuming and, consequently, cost-intensive task that doomed refactoring to be marginalized in production cycles. In this diploma thesis we argue that detection and correction of code Waws should be treated as inseparable tasks to annihilate parts of code that “do not feel right”. By reusing previous research conclusions made by the detection community and amalgamating it with recent achievements in the Veld of automated correction, we are able to present a holistic framework— Smeagol—that supports the continuous improvement of code. The contribution of this diploma thesis is as follows: First, we give an overview about the current state of the art in detection and correction and argumentatively deduce and discuss the need for a framework that assists developers in both steps, as both research Velds are investigated as separate topics up till now. Based on our argumentation, we will describe how a holistic framework can be designed and will describe our choices in detail by elaborating on the concrete implementation and techniques used for identifying and correcting code Waws. By giving workWow examples of how to extend our framework to detect and correct poor code, we show the practical usability and extendability of our tool. We Vnally compare our framework to competing approaches, point out diUerences and similarities and draw an overall conclusion of the presented work.