More Web Proxy on the site http://driver.im/

research-article

JNose: Java Test Smell Detector

Authors:

Tássio Virgínio,

Railana Santana,

Ivan MachadoAuthors Info & Claims

SBES '20: Proceedings of the XXXIV Brazilian Symposium on Software Engineering

Pages 564 - 569

https://doi.org/10.1145/3422392.3422499

Published: 21 December 2020 Publication History

Abstract

Several strategies have been proposed for test quality measurement and analysis. Code coverage is likely the most widely used one. It enables to verify the ability of a test case to cover as many source code branches as possible. Although code coverage has been widely used, novel strategies have been recently employed. It is the case of test smells analysis, which has been introduced as an affordable strategy to evaluate the quality of test code. Test smells are poor design choices in implementation, and their occurrence in test code might reduce the quality of test suites. Test smells identification is clearly dependent on tool support, otherwise it could become a cost-ineffective strategy. However, as far as we know, there is no tool that combines code coverage and test smells to address test quality measurement. In this work, we present the JNose Test, a tool aimed to analyze test suite quality in the perspective of test smells. JNose Test detects code coverage and software evolution metrics and a set of test smells throughout software versions.

References

[1]

Gabriele Bavota, Abdallah Qusef, Rocco Oliveto, Andrea De Lucia, and Dave Binkley. 2015. Are test smells really harmful? An empirical study. Empirical Software Engineering 20, 4 (2015), 1052--1094.

Digital Library

[2]

Arie Deursen, Leon M.F. Moonen, A. Bergh, and Gerard Kok. 2001. Refactoring Test Code. In Refactoring Test Code. CWI (Centre for Mathematics and Computer Science), Amsterdam, The Netherlands, The Netherlands.

[3]

Rahul Gopinath, Carlos Jensen, and Alex Groce. 2014. Code Coverage for Suite Evaluation by Developers. In Proceedings of the 36th International Conference on Software Engineering (ICSE 2014). ACM, New York, NY, USA, 72--82.

Digital Library

[4]

Giovanni Grano, Fabio Palomba, Dario Di Nucci, Andrea De Lucia, and Harald C Gall. 2019. Scented since the beginning: On the diffuseness of test smells in automatically generated test code. Journal of Systems and Software 156 (2019), 312--327.

Digital Library

[5]

Dayne Guerra Calle, Julien Delplanque, and Stéphane Ducasse. 2019. Exposing Test Analysis Results with DrTests. In International Workshop on Smalltalk Technologies. HAL, Cologne, Germany, 1--5. https://hal.inria.fr/hal-02404040

[6]

Negar Koochakzadeh and Vahid Garousi. 2010. TeCReVis: A Tool for Test Coverage and Test Redundancy Visualization. In Testing - Practice and Research Techniques, Leonardo Bottaci and Gordon Fraser (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 129--136.

[7]

Koochakzadeh Negar and Vahid Garousi. 2010. A Tester-Assisted Methodology for Test Redundancy Detection. Advances in Software Engineering 2010 (01 2010). https://doi.org/10.1155/2010/932686

[8]

Fabio Palomba, Andy Zaidman, and Andrea De Lucia. 2018. Automatic Test Smell Detection Using Information Retrieval Techniques. In IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, Madrid, Spain, 311--322.

[9]

Anthony Peruma, Khalid Almalki, Christian D. Newman, Mohamed Wiem Mkaouer, Ali Ouni, and Fabio Palomba. 2019. On the Distribution of Test Smells in Open Source Android Applications: An Exploratory Study. In Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering (CASCON '19). IBM Corp., Riverton, NJ, USA.

Digital Library

[10]

Davide Spadini, Fabio Palomba, Andy Zaidman, Magiel Bruntink, and Alberto Bacchelli. 2018. On the Relation of Test Smells to Software Code Quality. In International Conference on Software Maintenance and Evolution (ICSME). IEEE, Madrid, Spain, 1--12.

[11]

Tricentis. 2018. Software Fail Watch: 5th Edition. https://www.tricentis.com/resources/software-fail-watch-5th-edition/ Last access: June 2020.

[12]

Tássio Virgínio, Santana Railana, Luana Almeida Martins, Larissa Rocha Soares, Heitor Costa, and Ivan Machado. 2019. On the Influence of Test Smells on Test Coverage. In Proceedings of the XXXIII Brazilian Symposium on Software Engineering (SBES 2019). ACM, New York, NY, USA, 467--471.

Digital Library

[13]

Tássio Virgínio, Luana Martins, Larissa Rocha Soares, Santana Railana, Heitor Costa, and Ivan Machado. 2020. An empirical study of automatically-generated tests from the perspective of test smells. In Proceedings of the XXXIV Brazilian Symposium on Software Engineering (SBES 2020). ACM, New York, NY, USA, 5.

Digital Library

[14]

Vahid Garousi Yusifoğlu, Yasaman Amannejad, and Aysu Betin Can. 2015. Software test-code engineering: A systematic mapping. Information and Software Technology 58 (2015), 123--147.

Cited By

Lopes GRomão DSoares ERibeiro MAmaral GGheyi RMachado I(2024)A Road to Find Them All: Towards an Agnostic Strategy for Test Smell DetectionProceedings of the XXIII Brazilian Symposium on Software Quality10.1145/3701625.3701662(231-241)Online publication date: 5-Nov-2024
https://dl.acm.org/doi/10.1145/3701625.3701662
Fasolino ATramontana PCoppola RArdito LFraser GLeotta M(2024)Test Smells Learning by a Gamification ApproachProceedings of the 3rd ACM International Workshop on Gamification in Software Development, Verification, and Validation10.1145/3678869.3685687(30-33)Online publication date: 13-Sep-2024
https://dl.acm.org/doi/10.1145/3678869.3685687
Sharma TSpinellis DConstantinou EBacchelli A(2024)Multi-faceted Code Smell Detection at Scale using DesigniteJava 2.0Proceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644881(284-288)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644881
Show More Cited By

Index Terms

JNose: Java Test Smell Detector
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Empirical software validation
      2. Software defect analysis
        Software testing and debugging

Recommendations

An empirical study of automatically-generated tests from the perspective of test smells
SBES '20: Proceedings of the XXXIV Brazilian Symposium on Software Engineering

Developing test code can be as or more expensive than developing production code. Commonly, developers use automated unit test generators to speed up software testing. The purpose of such tools is to shorten production time without decreasing code ...
From Fine-tuning to Output: An Empirical Investigation of Test Smells in Transformer-Based Test Code Generation
SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing

Researchers have recently leveraged transformer-based test code generation models to improve testing-related tasks (e.g., assert completion and test method generation). One such model, AthenaTest, has been recently introduced, and it has been well ...
DeFlaker: automatically detecting flaky tests
ICSE '18: Proceedings of the 40th International Conference on Software Engineering

Developers often run tests to check that their latest changes to a code repository did not break any previously working functionality. Ideally, any new test failures would indicate regressions caused by the latest changes. However, some test failures may ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

SBES '20: Proceedings of the XXXIV Brazilian Symposium on Software Engineering

October 2020

901 pages

ISBN:9781450387538

DOI:10.1145/3422392

General Chairs:
Everton Cavalcante
UFRN, Brazil
,
Francisco Dantas
UERN, Brazil
,
Thais Batista
UFRN, Brazil

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

SBC: Brazilian Computer Society

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 December 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

SBES '20

SBES '20: 34th Brazilian Symposium on Software Engineering

October 21 - 23, 2020

Natal, Brazil

Acceptance Rates

Overall Acceptance Rate 147 of 427 submissions, 34%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

23
Total Citations
View Citations
317
Total Downloads

Downloads (Last 12 months)53
Downloads (Last 6 weeks)12

Reflects downloads up to 31 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Lopes GRomão DSoares ERibeiro MAmaral GGheyi RMachado I(2024)A Road to Find Them All: Towards an Agnostic Strategy for Test Smell DetectionProceedings of the XXIII Brazilian Symposium on Software Quality10.1145/3701625.3701662(231-241)Online publication date: 5-Nov-2024
https://dl.acm.org/doi/10.1145/3701625.3701662
Fasolino ATramontana PCoppola RArdito LFraser GLeotta M(2024)Test Smells Learning by a Gamification ApproachProceedings of the 3rd ACM International Workshop on Gamification in Software Development, Verification, and Validation10.1145/3678869.3685687(30-33)Online publication date: 13-Sep-2024
https://dl.acm.org/doi/10.1145/3678869.3685687
Sharma TSpinellis DConstantinou EBacchelli A(2024)Multi-faceted Code Smell Detection at Scale using DesigniteJava 2.0Proceedings of the 21st International Conference on Mining Software Repositories10.1145/3643991.3644881(284-288)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3643991.3644881
Yang YHu XXia XYang X(2024)The Lost World: Characterizing and Detecting Undiscovered Test SmellsACM Transactions on Software Engineering and Methodology10.1145/363197333:3(1-32)Online publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1145/3631973
Fushihara YAman HAmasaki SYokogawa TKawahara M(2024)Fault-Proneness of Python Programs Tested By Smelled Test Code2024 50th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)10.1109/SEAA64295.2024.00063(373-378)Online publication date: 28-Aug-2024
https://doi.org/10.1109/SEAA64295.2024.00063
Bhrugumalla SJha A(2024)TREC: A Regression Test Recommender for Java Projects2024 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME58944.2024.00100(903-907)Online publication date: 6-Oct-2024
https://doi.org/10.1109/ICSME58944.2024.00100
Martins LGhaleb TCosta HMachado I(2024)A comprehensive catalog of refactoring strategies to handle test smells in Java-based systemsSoftware Quality Journal10.1007/s11219-024-09663-732:2(641-679)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s11219-024-09663-7
Pizzini AReinehr SMalucelli A(2023)Sentinel: A process for automatic removing of Test SmellsProceedings of the XXII Brazilian Symposium on Software Quality10.1145/3629479.3630019(80-89)Online publication date: 7-Nov-2023
https://dl.acm.org/doi/10.1145/3629479.3630019
Campos DMartins LBezerra CMachado I(2023)Investigating Developers' Contributions to Test Smell Survivability: A Study of Open-Source ProjectsProceedings of the 8th Brazilian Symposium on Systematic and Automated Software Testing10.1145/3624032.3624044(86-95)Online publication date: 25-Sep-2023
https://dl.acm.org/doi/10.1145/3624032.3624044
Fatima SGhaleb TBriand L(2023)Flakify: A Black-Box, Language Model-Based Predictor for Flaky TestsIEEE Transactions on Software Engineering10.1109/TSE.2022.320120949:4(1912-1927)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TSE.2022.3201209
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents