Smells in system user interactive tests

Rwemalika, Renaud; Habchi, Sarra; Papadakis, Mike; Traon, Yves Le; Brasseur, Marie-Claude

doi:10.1007/s10664-022-10251-1

Cited by 6 publications

(4 citation statements)

References 73 publications

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“…In addition, Rwemalika et al. ( 2023 ) investigated test smells in interactive user test cases, finding that these are highly diffused and potentially harmful. Furthermore, Spadini et al.…”

Section: Related Workmentioning

confidence: 99%

“…Similar results were later confirmed (Martins et al 2023) and achieved when considering automatically generated test cases and in software systems developed using the combination of Scala and ScalaTest (De ). In addition, Rwemalika et al (2023) investigated test smells in interactive user test cases, finding that these are highly diffused and potentially harmful. Furthermore, Spadini et al (2018) showed that test smells impact the maintainability of both test and production code.…”

Section: Related Workmentioning

confidence: 99%

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Machine learning-based test smell detection

Pontillo,

Amoroso d’Aragona,

Pecorelli

et al. 2024

Empir Software Eng

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Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and effectiveness. Therefore, researchers have been proposing automated, heuristic-based techniques to detect them. However, the performance of these detectors is still limited and dependent on tunable thresholds. We design and experiment with a novel test smell detection approach based on machine learning to detect four test smells. First, we develop the largest dataset of manually-validated test smells to enable experimentation. Afterward, we train six machine learners and assess their capabilities in within- and cross-project scenarios. Finally, we compare the ML-based approach with state-of-the-art heuristic-based techniques. The key findings of the study report a negative result. The performance of the machine learning-based detector is significantly better than heuristic-based techniques, but none of the learners able to overcome an average F-Measure of 51%. We further elaborate and discuss the reasons behind this negative result through a qualitative investigation into the current issues and challenges that prevent the appropriate detection of test smells, which allowed us to catalog the next steps that the research community may pursue to improve test smell detection techniques.

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“…In addition, Rwemalika et al. ( 2023 ) investigated test smells in interactive user test cases, finding that these are highly diffused and potentially harmful. Furthermore, Spadini et al.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Machine learning-based test smell detection

Pontillo,

Amoroso d’Aragona,

Pecorelli

et al. 2024

Empir Software Eng

View full text Add to dashboard Cite

show abstract

“…How Good Are My Tests? [49] Conference Paper (P) Categorising test smells [60] Thesis (T) Smells in software test code: A survey of knowledge in industry and academia [58] Conference Paper (P) Enhancing developers' awareness on test suites' quality with test smell summaries [63] Thesis (T) Hunting for smells in natural language tests [54] Conference Paper (P) Smells in system user interactive tests [68] Conference Paper (P) Assessing diffusion and perception of test smells in scala projects [55] Conference Paper (P) Are test smells really harmful? An empirical study [56] Conference Paper (P) On the diffusion of test smells in automatically generated test code: An empirical study [57] Conference Paper (P)…”

Section: Data Availability Statementmentioning

confidence: 99%

Exploring the Connection between the TDD Practice and Test Smells—A Systematic Literature Review

Marabesi,

García-Holgado,

García-Peñalvo

2024

Computers

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Test-driven development (TDD) is an agile practice of writing test code before production code, following three stages: red, green, and refactor. In the red stage, the test code is written; in the green stage, the minimum code necessary to make the test pass is implemented, and in the refactor stage, improvements are made to the code. This practice is widespread across the industry, and various studies have been conducted to understand its benefits and impacts on the software development process. Despite its popularity, TDD studies often focus on the technical aspects of the practice, such as the external/internal quality of the code, productivity, test smells, and code comprehension, rather than the context in which it is practiced. In this paper, we present a systematic literature review using Scopus, Web of Science, and Google Scholar that focuses on the TDD practice and the influences that lead to the introduction of test smells/anti-patterns in the test code. The findings suggest that organizational structure influences the testing strategy. Additionally, there is a tendency to use test smells and TDD anti-patterns interchangeably, and test smells negatively impact code comprehension. Furthermore, TDD styles and the relationship between TDD practice and the generation of test smells are frequently overlooked in the literature.

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“…Sequential software development was the norm until the late 1990s. Only at the very end of the software development process do stakeholders pay attention to the smallest of details and prioritise quality above speed [13]. Additionally, the testing and deployment stages.…”

Section: Fig 1: Shift Left Testing Stagesmentioning

confidence: 99%

Shift left testing paradigm process implementation for quality of software based on fuzzy

et al. 2023

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Traditionally, testing is done first at end of the design phase, however this is no longer the case. Testing, finding, and categorising bugs, as well as releasing the development changes into the product, carries a price tag. If the test/verification team discovers a high-severity issue at the end of the lifecycle, the costs may climb. Even if all of the issues are resolved, the release could be delayed. Shift-Left testing is done in isolation by the test/verification team and does not increase testing time, but it has demonstrated to be in sync with product development in some cases. In the context of a process, shifting-left refers to taking action early on. Shift-left testing refers to the practise of testing software earlier in the development cycle than is customary, or to the left in the delivery pipeline, as opposed to the traditional practise of testing software later in the development cycle. Shifting to a "shift left" strategy assumes that the software development team may find bugs faster if they test their code as it is being written, rather than waiting until the end of the project based on fuzzy. Before the code is available for testing, shift left testing encourages developers to write test cases. An agile software development strategy known as "shift left" stresses putting test cases in place early in the life cycle of a project rather than at the conclusion. It also means that automated tests will cover a larger portion of a project's planned functionality rather to just a small portion. The shift left testing adoption benefits the organization to reduce the development cost and time as the testing is done along with development to avoid delay in the process. This paper analyse the benefits of organizations who adopted shift left testing in the software development process.

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Smells in system user interactive tests

Cited by 6 publications

References 73 publications

Machine learning-based test smell detection

Machine learning-based test smell detection

Exploring the Connection between the TDD Practice and Test Smells—A Systematic Literature Review

Shift left testing paradigm process implementation for quality of software based on fuzzy

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