Assessing Diffusion and Perception of Test Smells in Scala Projects

Bleser, Jonas De; Nucci, Dario Di; Roover, Coen De

doi:10.1109/msr.2019.00072

Cited by 32 publications

(23 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, De Bleser et al [74] With respect to the papers described so far, our work can be considered complementary. Indeed, to the best of our knowledge the empirical study proposed in this paper is the first one investigating the prominence of test smells in automatically generated test code.…”

Section: Indirect Testing (It)mentioning

confidence: 88%

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Indirect Testing (It)mentioning

confidence: 88%

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…The distribution of the metrics across the observed projects for each test smell can be found in the replication package [28]. [7,10]. However, since our derived thresholds are higher, the diffusion of test smells would decrease.…”

Section: Rq1: Severity Thresholdsmentioning

confidence: 99%

Investigating Severity Thresholds for Test Smells

Spadini

Schvarcbacher

Oprescu

et al. 2020

Proceedings of the 17th International Conference on Mining Software Repositories

View full text Add to dashboard Cite

Test smells are poor design decisions implemented in test code, which can have an impact on the effectiveness and maintainability of unit tests. Even though test smell detection tools exist, how to rank the severity of the detected smells is an open research topic. In this work, we aim at investigating the severity rating for four test smells and investigate their perceived impact on test suite maintainability by the developers. To accomplish this, we first analyzed some 1,500 open-source projects to elicit severity thresholds for commonly found test smells. Then, we conducted a study with developers to evaluate our thresholds. We found that (1) current detection rules for certain test smells are considered as too strict by the developers and (2) our newly defined severity thresholds are in line with the participants' perception of how test smells have an impact on the maintainability of a test suite.

show abstract

“…Unlike the previous study, he opted for source code analysis based on string matching. De Bleser et al [2019] analyzed the tests of 164 Scala projects (1.7M LOC) for a diffusion of test smells. They used a similar way of assembling a corpus.…”

Section: Related Workmentioning

confidence: 99%

Scala implicits are everywhere: a large-scale study of the use of Scala implicits in the wild

Křikava

Miller

Vítek

2019

Proc. ACM Program. Lang.

View full text Add to dashboard Cite

The Scala programming language offers two distinctive language features implicit parameters and implicit conversions, often referred together as implicits. Announced without fanfare in 2004, implicits have quickly grown to become a widely and pervasively used feature of the language. They provide a way to reduce the boilerplate code in Scala programs. They are also used to implement certain language features without having to modify the compiler. We report on a large-scale study of the use of implicits in the wild. For this, we analyzed 7,280 Scala projects hosted on GitHub, spanning over 8.1M call sites involving implicits and 370.7K implicit declarations across 18.7M lines of Scala code. CCS Concepts: • Software and its engineering → Language features; General programming languages.

show abstract

Assessing Diffusion and Perception of Test Smells in Scala Projects

Cited by 32 publications

References 25 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

Investigating Severity Thresholds for Test Smells

Scala implicits are everywhere: a large-scale study of the use of Scala implicits in the wild

Contact Info

Product

Resources

About