On the Impact of Refactoring Operations on Code Quality Metrics

Abstract

Abstract-Refactorings are behavior-preserving source code transformations. While tool support exists for (semi)automatically identifying refactoring solutions, applying or not a recommended refactoring is usually up to the software developers, who have to assess the impact that the transformation will have on their system. Evaluating the pros (e.g., the bad smell removal) and cons (e.g., side effects of the change) of a refactoring is far from trivial. We present RIPE (Refactoring Impact PrEdiction), a technique that estimates the impact of refactoring operations on source code quality metrics. RIPE supports 12 refactoring operations and 11 metrics and it can be used together with any refactoring recommendation tool. RIPE was used to estimate the impact on 8,103 metric values, for 504 refactorings from 15 open source systems. 38% of the estimates are correct, whereas the median deviation of the estimates from the actual values is 5% (with a 31% average).

When and Why Your Code Starts to Smell Bad

“…This result is quite surprising, given that one of the goals behind refactoring is the removal of bad smells [20]. This finding highlights the need for techniques and tools aimed at assessing the impact of refactoring operations on source code before their actual application (e.g., see the recent work by Chaparro et al [14]). Lesson 4.…”

Why we refactor? confessions of GitHub contributors

“…As we rely on RefactoringMiner to find refactorings performed in the version history of software repositories, it is important to estimate its recall and precision. For this reason, we evaluated RefactoringMiner using the dataset reported in a study by Chaparro et al [5]. This dataset includes a list of refactorings performed by two Ph.D. students on two software systems (ArgoUML and aTunes) along with the source code before and after the modifications.…”

RefDiff: Detecting Refactorings in Version Histories

“…In a second study that mined refactorings in 285 GitHub hosted Java repositories, Silva et al [6] found 1,030 false positives out of 2,441 refactorings (63% precision). However, the authors also evaluated Refactoring Miner using as a benchmark the dataset reported by Chaparro et al [21], in which it achieved 93% precision and 98% recall.…”

“…To be able to reliably compute recall, we employed the strategy of building an evaluation oracle by deliberately applying refactoring in software repositories in a controlled manner, similarly to Chaparro et al [21]. Such refactorings were applied by graduate students of a Software Architecture course.…”