Fetching the paper…
Reading the bibliography…
Test smells are coding issues that typically arise from inadequate practices, a lack of knowledge about effective testing, or deadline pressures to complete projects.
Refactoring test code. In International conference on extreme programming and flexible processes in software engineering . 92–95
Arie van Deursen, Leon Moonen, Alex van Den Bergh, and Gerard Kok. 2001 · 2001
Earlier work this paper cites.
Test-driven development: by example
K. Beck. 2003 · 2003
Earlier work this paper cites.
xUnit test patterns: Refactoring test code
G. Meszaros. 2007 · 2007
Earlier work this paper cites.
On The Detection of Test Smells: A Metrics-Based Approach for General Fixture and Eager Test
Bart Van Rompaey, Bart Du Bois, Serge Demeyer, and Matthias Rieger. 2007 · 2007
Earlier work this paper cites.
Hunting for smells in natural language tests. In International Conference on Software Engineering . IEEE Computer Society, 1217–1220
Benedikt Hauptmann, Maximilian Junker, Sebastian Eder, Lars Heinemann, Rudolf Vaas, and Peter Braun. 2013 · 2013
Earlier work this paper cites.
Are test smells really harmful? An empirical study
Gabriele Bavota, Abdallah Qusef, Rocco Oliveto, Andrea De Lucia, and Dave Binkley. 2015 · 2015
Earlier work this paper cites.
Metamorphic Testing: A Review of Challenges and Opportunities
Tsong Yueh Chen, Fei-Ching Kuo, Huai Liu, Pak-Lok Poon, Dave Towey, T. H. Tse, and Zhi Quan Zhou. 2018 · 2018
Earlier work this paper cites.
On the relation of test smells to software code quality. In International conference on software maintenance and evolution . IEEE, 1–12
Davide Spadini, Fabio Palomba, Andy Zaidman, Magiel Bruntink, and Alberto Bacchelli. 2018 · 2018
Earlier work this paper cites.
Assessing Diffusion and Perception of Test Smells in Scala Projects. In International Conference on Mining Software Repositories . 457–467
Jonas De Bleser, Dario Di Nucci, and Coen De Roover. 2019 · 2019
Earlier work this paper cites.
On the Distribution of Test Smells in Open Source Android Applications: An Exploratory Study. In International Conference on Computer Science and Software Engineering . 193–202
Anthony Peruma, Khalid Almalki, Christian D. Newman, Mohamed Wiem Mkaouer, Ali Ouni, and Fabio Palomba. 2019 · 2019
Earlier work this paper cites.
Language Models are Unsupervised Multitask Learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Earlier work this paper cites.
A survey on test practitioners’ awareness of test smells. In Iberoamerican Conference on Software Engineering . Curran Associates, 462–475
Nildo Silva Junior, Larissa Rocha, Luana Almeida Martins, and Ivan Machado. 2020 · 2020
Earlier work this paper cites.
Just-In-Time Test Smell Detection and Refactoring: The DARTS Project. In International Conference on Program Comprehension . 441–445
Stefano Lambiase, Andrea Cupito, Fabiano Pecorelli, Andrea De Lucia, and Fabio Palomba. 2020 · 2020
Cited alongside, same era.
RTj: A Java Framework for Detecting and Refactoring Rotten Green Test Cases. In International Conference on Software Engineering: Companion Proceedings . 69–72
Matias Martinez, Anne Etien, Stéphane Ducasse, and Christopher Fuhrman. 2020 · 2020
Cited alongside, same era.
Retraction Note: Retraction note to: The smell of fear: on the relation between test smells and flaky tests
Fabio Palomba and Andy Zaidman. 2020 · 2020
Cited alongside, same era.
RAIDE: A Tool for Assertion Roulette and Duplicate Assert Identification and Refactoring. In 34th Brazilian Symposium on Software Engineering (SBES) . 374–379
Railana Santana, Luana Martins, Larissa Rocha, Tássio Virgínio, Adriana Cruz, Heitor Costa, and Ivan Machado. 2020 · 2020
Cited alongside, same era.
An empirical evaluation of RAIDE: A semi-automated approach for test smells detection and refactoring
Railana Santana, Luana Martins, Tássio Virgínio, Larissa Rocha, Heitor Costa, and Ivan Machado. 2024 · 2023
Later among the works it cites.
Refactoring Test Smells With JUnit 5: Why Should Developers Keep Up-to-Date?
Elvys Soares, Márcio Ribeiro, Rohit Gheyi, Guilherme Amaral, and André L. M. Santos. 2023b · 2023
Later among the works it cites.
The Open Catalog of Test Smells
2024 · 2024
Closest in time.
Prompt Engineering Guide
DAIR.AI. 2024 · 2024
Closest in time.
Large Language Models for Software Engineering: A Systematic Literature Review
Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, and Haoyu Wang. 2024 · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Refactoring Test Smells: A Perspective from Open-Source Developers. In Brazilian Symposium on Systematic and Automated Software Testing . 50–59
Elvys Soares, Márcio Ribeiro, Guilherme Amaral, Rohit Gheyi, Leo Fernandes, Alessandro Garcia, Baldoino Fonseca, and André Santos. 2020 · 2020
Cited alongside, same era.
Investigating Severity Thresholds for Test Smells. In International Conference on Mining Software Repositories (MSR) . 311–321
Davide Spadini, Martin Schvarcbacher, Ana-Maria Oprescu, Magiel Bruntink, and Alberto Bacchelli. 2020 · 2020
Cited alongside, same era.
Test Smell Detection Tools: A Systematic Mapping Study. In International Conference on Evaluation and Assessment in Software Engineering . 170–180
Wajdi Aljedaani, Anthony Peruma, Ahmed Aljohani, Mazen Alotaibi, Mohamed Wiem Mkaouer, Ali Ouni, Christian D. Newman, Abdullatif Ghallab, and Stephanie Ludi. 2021 · 2021
Cited alongside, same era.
Developers perception on the severity of test smells: an empirical study
Denivan Campos, Larissa Rocha, and Ivan Machado. 2021 · 2021
Cited alongside, same era.
How are test smells treated in the wild? A tale of two empirical studies
Nildo Silva Junior, Luana Martins, Larissa Rocha, Heitor Costa, and Ivan Machado. 2021 · 2021
Cited alongside, same era.
The secret life of test smells-an empirical study on test smell evolution and maintenance
Dong Jae Kim, Tse-Hsun Chen, and Jinqiu Yang. 2021 · 2021
Cited alongside, same era.
Test smells 20 years later: detectability, validity, and reliability
Annibale Panichella, Sebastiano Panichella, Gordon Fraser, Anand Ashok Sawant, and Vincent J Hellendoorn. 2022 · 2022
Cited alongside, same era.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023 · 2023
Cited alongside, same era.
A Catalog of Transformations to Remove Smells From Natural Language Tests. In International Conference on Evaluation and Assessment in Software Engineering . ACM, 7–16
Manoel Aranda III, Naelson Oliveira, Elvys Soares, Márcio Ribeiro, Davi Romão, Ullyanne Patriota, Rohit Gheyi, Emerson Souza, and Ivan Machado. 2024 · 2024
Closest in time.
Evaluating Large Language Models in Detecting Test Smells (artifacts)
Keila Lucas, Rohit Gheyi, Elvys Soares, Márcio Ribeiro, and Ivan Machado. 2024 · 2024
Closest in time.
Machine learning-based test smell detection
Valeria Pontillo, Dario Amoroso d’Aragona, Fabiano Pecorelli, Dario Di Nucci, Filomena Ferrucci, and Fabio Palomba. 2024 · 2024
Closest in time.
Breaking the Silence: the Threats of Using LLMs in Software Engineering. In International Conference on Software Engineering - New Ideas and Emerging Results . ACM/IEEE
June Sallou, Thomas Durieux, and Annibale Panichella. 2024 · 2024
Closest in time.
Software testing with large language models: Survey, landscape, and vision
Junjie Wang, Yuchao Huang, Chunyang Chen, Zhe Liu, Song Wang, and Qing Wang. 2024 · 2024
Closest in time.
The Lost World: Characterizing and Detecting Undiscovered Test Smells
Yanming Yang, Xing Hu, Xin Xia, and Xiaohu Yang. 2024 · 2024
Closest in time.