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

Palomba, Fabio; Panichella, Annibale; Zaidman, Andy; Oliveto, Rocco; Lucia, Andrea De

doi:10.1109/tse.2017.2752171

Cited by 70 publications

(58 citation statements)

References 99 publications

(206 reference statements)

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“…Moreover, most of the times developers are aware of the presence of code smells, but they deliberately postpone their removal [60] to avoid APIs modifications [14] or simply because developers do not perceive them as actual problems [18], [21]. Finally, a recent study [61] found significant differences in the way code smells detected using different sources of information evolve over time: specifically, developers tend to maintain and refactor more code smells identified using textual information, while design problems affected by structural issues (e.g., too many dependencies between classes) are more difficult to understand and, therefore, more difficult to manage [61].…”

Section: Related Literature On Code Smellsmentioning

confidence: 99%

Toward a Smell-Aware Bug Prediction Model

Palomba

Zanoni

Fontana

et al. 2019

IIEEE Trans. Software Eng.

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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.

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Section: Related Literature On Code Smellsmentioning

confidence: 99%

Toward a Smell-Aware Bug Prediction Model

Palomba

Zanoni

Fontana

et al. 2019

IIEEE Trans. Software Eng.

Self Cite

View full text Add to dashboard Cite

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“…Over the last decade the research community spent a considerable effort in studying (e.g., [1], [3], [32], [39], [51], [55], [59], [61], [66], [72], [78]- [80]) and detecting (e.g., [33], [36], [41], [43], [46], [49], [52], [54], [70]) design flaws occurring in production code, also known as code smells [23]. At the same time, problems concerning the design of test code have only been partially explored and our literature survey showed us that our empirical knowledge is still limited.…”

Section: Related Workmentioning

confidence: 99%

“…If a commit message matches an issue ID present in the issue tracker or it contains keywords such as 'bug', 'fix', or 'defect', we consider it as a bug fixing activity. This approach has been extensively used in the past to determine bug fixing changes [29], [34] and it has an accuracy close to 80% [22], [55], thus we deem it as being accurate enough for our study. Once we have detected all the bug fixing commits involving a test method, we employ SZZ to obtain the commits where the bug was introduced.…”

Section: B Data Extractionmentioning

confidence: 99%

On the Relation of Test Smells to Software Code Quality

Spadini

Palomba

Zaidman

et al. 2018

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

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112

108

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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.

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“…Fowler defined "bad code smells" (shortly, "code smells" or simply "smells") as "symptoms of the presence of poor design or implementation choices applied during the development of a software system" [35]. Starting from there, several researchers heavily investigated (i) how code smells evolve over time [84,86,91,119,120,121], (ii) the way developers perceive them [79,111,126], and (iii) what is their impact on non-functional attributes of source code [1,36,50,52,77,83,104,125]. All these studies came up with a shared conclusion: code smells negatively impact program comprehension, maintainability of source code, and development costs.…”

Section: Code Smell Detection and Prioritizationmentioning

confidence: 99%

Improving change prediction models with code smell-related information

et al. 2019

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Code smells represent sub-optimal implementation choices applied by developers when evolving software systems. The nagative impact of code smells has been widely investigated in the past: besides developers' productivity and ability to comprehend source code, researchers empirically showed that the presence of code smells heavily impacts the change-proneness of the affected classes. On the basis of these findings, in this paper we conjecture that code smell-related information can be effectively exploited to improve the performance of change prediction models, i.e., models having as goal that of indicating to developers which classes are more likely to change in the future, so that they may apply preventive maintenance actions. Specifically, we exploit the so-called intensity index -a previously defined metric that captures the severity of a code smell-and evaluate its contribution when added as additional feature in the context of three state of the art change prediction models based on product, process, and developer-based features. We also compare the performance achieved by the proposed model with the one of an alternative technique that considers the previously defined antipattern metrics, namely a set of indicators computed considering the history of code smells in files. Our results report that (i) the prediction performance of the intensity-including

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

Cited by 70 publications

References 99 publications

Toward a Smell-Aware Bug Prediction Model

Toward a Smell-Aware Bug Prediction Model

On the Relation of Test Smells to Software Code Quality

Improving change prediction models with code smell-related information

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