A large-scale empirical study on the lifecycle of code smell co-occurrences

Palomba, Fabio; Bavota, Gabriele; Penta, Massimiliano Di; Fasano, Fausto; Oliveto, Rocco; Lucia, Andrea De

doi:10.1016/j.infsof.2018.02.004

Cited by 89 publications

(40 citation statements)

References 15 publications

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“…Like production code, test code should also be designed following good programming practices [32]. During the last decade, the research community spent a lot of effort on the definition of methods and tools for detecting design flaws in production code [33][34][35][36][37][38][39][40][41][42][43], as well as empirical studies aimed at assessing their impact on maintainability [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62].…”

Section: Code Smells In Test Casesmentioning

confidence: 99%

Scented since the beginning: On the diffuseness of test smells in automatically generated test code

Grano

Palomba

Nucci

et al. 2019

Journal of Systems and Software

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Software testing represents a key software engineering practice to ensure source code quality and reliability. To support developers in this activity and reduce testing effort, several automated unit test generation tools have been proposed. Most of these approaches have the main goal of covering as more branches as possible. While these approaches have good performance, little is still known on the maintainability of the test code they produce, i.e., whether the generated tests have a good code quality and if they do not possibly introduce issues threatening their effectiveness. To bridge this gap, in this paper we study to what extent existing automated test case generation tools produce potentially problematic test code. We consider seven test smells, i.e., suboptimal design choices applied by programmers during the development of test cases, as measure of code quality of the generated tests, and evaluate their diffuseness in the unit test classes automatically generated by three state-of-the-art tools such as Randoop, JTExpert, and Evosuite. Moreover, we investigate whether there are characteristics of test and production code influencing the generation of smelly tests. Our study shows that all the considered tools tend to generate a high quantity of two specific test smell types, i.e., Assertion Roulette and Eager Test, which are those that previous studies showed to negatively impact the reliability of production code. We also discover that test size is correlated with the generation of smelly tests. Based on our findings, we argue that more effective automated generation algorithms that explicitly take into account test code quality should be further investigated and devised.

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Section: Code Smells In Test Casesmentioning

confidence: 99%

Scented since the beginning: On the diffuseness of test smells in automatically generated test code

Grano

Palomba

Nucci

et al. 2019

Journal of Systems and Software

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“…Palomba et al [32] investigated the nature of code smell co-occurrences in 395 projects. The results showed that 59% of the smelly classes are affected by more than one code smell.…”

Section: Related Workmentioning

confidence: 99%

A large scale empirical study of the impact of Spaghetti Code and Blob anti-patterns on program comprehension

Politowski

Khomh

Romano

et al. 2020

Information and Software Technology

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Context: Several studies investigated the impact of anti-patterns (i.e., "poor" solutions to recurring design problems) during maintenance activities and reported that anti-patterns significantly affect the developers' effort required to edit files. However, before developers edit files, they must understand the source code of the systems. This source code must be easy to understand by developers. Objective: In this work, we provide a complete assessment of the impact of two instances of two anti-patterns, Blob or Spaghetti Code, on program comprehension. Method: We analyze the impact of these two anti-patterns through three empirical studies conducted at Polytechnique Montréal (Canada) with 24 participants; at Carlton University (Canada) with 30 participants; and at University Basilicata (Italy) with 79 participants. Results: We collect data from 372 tasks obtained thanks to 133 different participants from the three universities. We use three metrics to assess the developers' comprehension of the source code: (1) the duration to complete each task; (2) their percentage of correct answers; and, (3) the NASA task load index for their effort. Conclusions: We report that, although single occurrences of Blob or Spaghetti code anti-patterns have little effect on code comprehension, two occurrences of either Blob or Spaghetti Code significantly increases the developers' time spent in their tasks, reduce their percentage of correct answers, and increase their effort. Hence, we recommend that developers act on both anti-patterns, which should be refactored out of the source code whenever possible. We also recommend further studies on combinations of anti-patterns rather than on single anti-patterns one at a time.

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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%

“…Our observations might still have been threatened by the presence of a large number of code smell co-occurrences [86,125], which might have biased the intensity level of the smelly classes of our dataset. To account for this aspect, we measured the percentage of classes in our dataset affected by more than one smell: we only found that 8% of the classes, on average, contained more code smells.…”

Section: Threats To Construct Validitymentioning

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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A large-scale empirical study on the lifecycle of code smell co-occurrences

Cited by 89 publications

References 15 publications

Scented since the beginning: On the diffuseness of test smells in automatically generated test code

Scented since the beginning: On the diffuseness of test smells in automatically generated test code

A large scale empirical study of the impact of Spaghetti Code and Blob anti-patterns on program comprehension

Improving change prediction models with code smell-related information

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