A case study of refactoring large-scale industrial systems to efficiently improve source code quality

Refactoring source code has many benefits (e.g. improving maintainability, robustness and source code quality), but it takes time away from other implementation tasks, resulting in developers neglecting refactoring steps during the development process. But what happens when they know that the quality of their source code needs to be improved and they can get the extra time and money to refactor the code? What will they do? What will they consider the most important for improving source code quality? What sort of issues will they address first or last and how will they solve them? In our paper, we look for answers to these questions in a case study of refactoring large-scale industrial systems where developers participated in a project to improve the quality of their software systems. We collected empirical data of over a thousand refactoring patches for 5 systems with over 5 million lines of code in total, and we found that developers really optimized the refactoring process to significantly improve the quality of these systems. © 2014 Springer International Publishing

Automating the refactoring process

To decrease software maintenance cost, software development companies use static source code analysis techniques. Static analysis tools are capable of finding potential bugs, anti-patterns, coding rule violations, and they can also enforce coding style standards. Although there are several available static analyzers to choose from, they only support issue detection. The elimination of the issues is still performed manually by developers. Here, we propose a process that supports the automatic elimination of coding issues in Java. We introduce a tool that uses a third-party static analyzer as input and enables developers to automatically fix the detected issues for them. Our tool uses a special technique, called reverse AST-search, to locate source code elements in a syntax tree, just based on location information. Our tool was evaluated and tested in a two-year project with six software development companies where thousands of code smells were identified and fixed in five systems that have altogether over five million lines of code

Commits Analysis for Software Refactoring Documentation and Recommendation

Software projects frequently evolve to meet new requirements and/or to fix bugs. While this evolution is critical, it may have a negative impact on the quality of the system. To improve the quality of software systems, the first step is “detection" of code antipatterns to be restructured which can be considered as “refactoring opportunities". The second step is the “prioritization" of code fragments to be refactored/fixed. The third step is “recommendation" of refactorings to fix the detected quality issues. The fourth step is “testing" the recommended refactorings to evaluate their correctness. The fifth step is the “documentation" of the applied refactorings. In this thesis, we addressed the above five steps: 1. We designed a bi-level multi-objective optimization approach to enable the generation of antipattern examples that can improve the efficiency of detection rules for bad quality designs. 2. Regarding refactoring recommendations, we first identify refactoring opportunities by analyzing developer commit messages and quality of changed files, then we distill this knowledge into usable context driven refactoring recommendations to complement static and dynamic analysis of code. 3. We proposed an interactive refactoring recommendation approach that enables developers to pinpoint their preferences simultaneously in the objective (quality metrics) and decision (code location) spaces. 4. We proposed a semi-automated refactoring documentation bot that helps developers to interactively check and validate the documentation of the refactorings and/or quality improvements at the file level for each opened pull-request before being reviewed or merged to the master 5. We performed interviews with and a survey of practitioners as well as a quantitative analysis of 1,193 commit messages containing refactorings to establish a refactoring documentation model as a set of components. 6. We formulated the recommendation of code reviewers as a multi-objective search problem to balance the conflicting objectives of expertise, availability, and history of collaborations. 7. We built a dataset composed of 50,000+ composite code changes pertaining to more than 7,000 open-source projects. Then, we proposed and evaluated a new deep learning technique to generate commit messages for composite code changes based on an attentional encoder-decoder with two encoders and BERT embeddings.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/169486/1/Soumaya Rebai final dissertation.pdfDescription of Soumaya Rebai final dissertation.pdf : Dissertatio