The effect of refactoring on change and fault-proneness in commercial C# software

IIEEE Trans. Software Eng.

Panichella

Zaidman

et al. 2018

Abstract-Code smells are symptoms of poor design or implementation choices that have a negative effect on several aspects of software maintenance and evolution, such as program comprehension or change-and fault-proneness. This is why researchers have spent a lot of effort on devising methods that help developers to automatically detect them in source code. Almost all the techniques presented in literature are based on the analysis of structural properties extracted from source code, although alternative sources of information (e.g., textual analysis) for code smell detection have also been recently investigated. Nevertheless, some studies have indicated that code smells detected by existing tools based on the analysis of structural properties are generally ignored (and thus not refactored) by the developers. In this paper, we aim at understanding whether code smells detected using textual analysis are perceived and refactored by developers in the same or different way than code smells detected through structural analysis. To this aim, we set up two different experiments. We have first carried out a software repository mining study to analyze how developers act on textually or structurally detected code smells. Subsequently, we have conducted a user study with industrial developers and quality experts in order to qualitatively analyze how they perceive code smells identified using the two different sources of information. Results indicate that textually detected code smells are easier to identify and for this reason they are considered easier to refactor with respect to code smells detected using structural properties. On the other hand, the latter are often perceived as more severe, but more difficult to exactly identify and remove.

Section: Impact Of Code Smells On Non-functional Attributes Of Sourcesupporting

confidence: 57%

The Scent of a Smell: An Extensive Comparison Between Textual and Structural Smells

IIEEE Trans. Software Eng.

Panichella

Zaidman

et al. 2018

“…Palomba et al [26] confirmed such findings on a larger set of 13 code smell types, and also reported that the removal of code smells might be not always beneficial for improving source code maintainability. Also Gatrell and Counsell [62] and by Li and Shatnawi [63] confirmed the negative impact of code smells on faultproneness; in addition, they suggested that refactoring a class, besides improving the architectural quality, reduces the probability of the class having errors in the future [62], [63].…”

Section: Related Literature On Code Smellsmentioning

confidence: 92%

Toward a Smell-Aware Bug Prediction Model

IIEEE Trans. Software Eng.

Zanoni

Fontana

et al. 2019

Code smells are symptoms of poor design and implementation choices. Previous studies empirically assessed the impact of smells on code quality and clearly indicate their negative impact on maintainability, including a higher bug-proneness of components affected by code smells. In this paper, we capture previous findings on bug-proneness to build a specialized bug prediction model for smelly classes. Specifically, we evaluate the contribution of a measure of the severity of code smells (i.e., code smell intensity) by adding it to existing bug prediction models based on both product and process metrics, and comparing the results of the new model against the baseline models. Results indicate that the accuracy of a bug prediction model increases by adding the code smell intensity as predictor. We also compare the results achieved by the proposed model with the ones of an alternative technique which considers metrics about the history of code smells in files, finding that our model works generally better. However, we observed interesting complementarities between the set of buggy and smelly classes correctly classified by the two models. By evaluating the actual information gain provided by the intensity index with respect to the other metrics in the model, we found that the intensity index is a relevant feature for both product and process metrics-based models. At the same time, the metric counting the average number of code smells in previous versions of a class considered by the alternative model is also able to reduce the entropy of the model. On the basis of this result, we devise and evaluate a smell-aware combined bug prediction model that included product, process, and smell-related features.We demonstrate how such model classifies bug-prone code components with an F-Measure at least 13% higher than the existing state-of-the-art models.

“…Gatrell and Counsell [24] conducted an empirical study aimed at quantifying the effect of refactoring on class changeand defect-proneness. In particular, they monitored a commercial project for eight months and identified the refactoring operations applied by developers during the first four months.…”

Section: B Change-and Defect-proneness Of Code Smellsmentioning

confidence: 99%

“…To fill this gap, in this paper we quantitatively investigate the relationship between the presence of smells in test methods and the change-and defect-proneness of both these test methods and the production code they intend to test. Similar to several previous studies on software quality [24], [62], we employ the proxy metrics change-proneness (i.e., number of times a method changes between two releases) and defectproneness (i.e., number of defects the method had between two releases). We conduct an extensive observational study [15], collecting data from 221 releases of ten open source software systems, analyze more than a million test cases, and investigate the association between six test smell types and the aforementioned proxy metrics.…”

Section: Introductionmentioning

confidence: 99%

On the Relation of Test Smells to Software Code Quality

Spadini

2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)

Zaidman

et al. 2018

111

108

Test smells are sub-optimal design choices in the implementation of test code. As reported by recent studies, their presence might not only negatively affect the comprehension of test suites but can also lead to test cases being less effective in finding bugs in production code. Although significant steps toward understanding test smells, there is still a notable absence of studies assessing their association with software quality.In this paper, we investigate the relationship between the presence of test smells and the change-and defect-proneness of test code, as well as the defect-proneness of the tested production code. To this aim, we collect data on 221 releases of ten software systems and we analyze more than a million test cases to investigate the association of six test smells and their co-occurrence with software quality. Key results of our study include:(i) tests with smells are more change-and defect-prone, (ii) 'Indirect Testing', 'Eager Test', and 'Assertion Roulette' are the most significant smells for change-proneness and, (iii) production code is more defect-prone when tested by smelly tests.