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A Survey of Test Based Automatic Program Repair

Yuzhen Liu, Long Zhang, Zhenyu Zhang
2018 Journal of Software  
In this paper, we systematically survey the work in mainstream of test-based program repair (TBR) and discuss the properties automatically generated patches should have.  ...  Hence automatic program repair techniques, especially the test-based approaches, have drawn great attentions in recent years.  ...  [60] integrated metamorphic testing into automatic program repair to alleviate the oracle problem.  ... 
doi:10.17706/jsw.13.8.437-452 fatcat:dz3jksehabg2nptmugyuchghqu

Metamorphic Testing

Tsong Yueh Chen, Fei-Ching Kuo, Huai Liu, Pak-Lok Poon, Dave Towey, T. H. Tse, Zhi Quan Zhou
2018 ACM Computing Surveys  
, integration with other so ware engineering techniques, and the validation and evaluation of so ware systems.  ...  A number of strategies have been proposed to generate test cases for addressing the reliable test set problem, including random testing [39], coverage testing [101], search-based testing [40] , and symbolic  ...  of assertions, and the design of tness functions for search-based testing.  ... 
doi:10.1145/3143561 fatcat:bwa2rivy4rby7awczky2j3zx3a

2018 Index IEEE Transactions on Software Engineering Vol. 44

2019 IEEE Transactions on Software Engineering  
., þ, TSE Nov. 2018 1024-1038 Integer programming Formulating Criticality-Based Cost-Effective Fault Tolerance Strategies for Multi-Tenant Service-Based Systems.  ...  ., þ, TSE Aug. 2018 725-746 A PVS-Simulink Integrated Environment for Model-Based Analysis of Cyber-Physical Systems.  ... 
doi:10.1109/tse.2018.2887195 fatcat:sss2tw3g2bb2xpsrbh6oyrduti

Enhance Combinatorial Testing with Metamorphic Relations

Xintao Niu, Yanjie Sun, Huayao Wu, Gang Li, Nie Changhai, Yu Lei, Xiaoyin Wang
2021 IEEE Transactions on Software Engineering  
Several empirical studies conducted on 31 real-world software projects have shown that COMER increased the number of metamorphic groups by an average factor of 75.9 and also increased the failure detection  ...  COMER puts a high priority on generating pairs of test cases which match the input rules of MRs, i.e., the Metamorphic Group (MG), such that the correctness can be automatically determined by verifying  ...  For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited.  ... 
doi:10.1109/tse.2021.3131548 fatcat:426tadcz6zgwdn5nvtkdno45w4

Big Data Testing Techniques: Taxonomy, Challenges and Future Trends [article]

Iram Arshad, Saeed Hamood Alsamhi, Wasif Afzal
2022 arXiv   pre-print
In addition, the combinatorial testing technique is one of the most applied techniques in combination with other techniques (i.e., random testing, mutation testing, input space partitioning and equivalence  ...  Though numerous research efforts deal with Big Data testing, a comprehensive review to address testing techniques and challenges of Big Data is not available as yet.  ...  One is called MRTest-Random based on random testing and the second is called MRTest-t-Wise, which is based on equivalence partitioning with combinatorial testing (a kind of partition testing).  ... 
arXiv:2111.02853v4 fatcat:gui3cvgnabacrdedsvav7n5q3m

Fairness Testing: A Comprehensive Survey and Analysis of Trends [article]

Zhenpeng Chen, Jie M. Zhang, Max Hort, Federica Sarro, Mark Harman
2022 arXiv   pre-print
We collect 122 papers and organise them based on the testing workflow (i.e., the testing activities) and the testing components (i.e., where to find fairness bugs) for conducting fairness testing.  ...  Research has focused on helping software engineers to detect fairness bugs automatically. This paper provides a comprehensive survey of existing research on fairness testing.  ...  Many thanks to those authors who kindly provided comments and feedback on earlier drafts of this paper.  ... 
arXiv:2207.10223v2 fatcat:2k3zj2lr2fh7dhapetkm6irame

Machine Learning Testing: Survey, Landscapes and Horizons [article]

Jie M. Zhang University College London, Nanyang Technological University)
2019 arXiv   pre-print
It covers 144 papers on testing properties (e.g., correctness, robustness, and fairness), testing components (e.g., the data, learning program, and framework), testing workflow (e.g., test generation and  ...  This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research.  ...  Many thanks to those members of the community who kindly provided comments and feedback on earlier drafts of this paper.  ... 
arXiv:1906.10742v2 fatcat:p5c54cy4pjc5flzm7shybk3qxe

A Survey on Machine Learning Techniques for Source Code Analysis [article]

Tushar Sharma, Maria Kechagia, Stefanos Georgiou, Rohit Tiwari, Federica Sarro
2021 arXiv   pre-print
We summarize our observations and findings with the help of the identified studies.  ...  Method: We investigate studies belonging to twelve categories of software engineering tasks and corresponding machine learning techniques, tools, and datasets that have been applied to solve them.  ...  adaptive code search.  ... 
arXiv:2110.09610v1 fatcat:jc6c3jnxcbekfbssyy7hn3zwxa

A Taxonomic Review of Adaptive Random Testing: Current Status, Classifications, and Issues [article]

Jinfu Chen, Hilary Ackah-Arthur, Chengying Mao, Patrick Kwaku Kudjo
2019 arXiv   pre-print
We classify all ART studies and assess existing ART methods for numeric programs with a focus on their motivation, strategy, and findings.  ...  Efforts to mainly improve the failure-detection capability of RT, have led to the proposition of Adaptive Random Testing (ART).  ...  Chen for his helpful comments in an earlier version of this article.  ... 
arXiv:1909.10879v2 fatcat:cgm7j2ipejeuzpja7x7qn3dhui

Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code [article]

Patrick Bareiß, Beatriz Souza, Marcelo d'Amorim, Michael Pradel
2022 arXiv   pre-print
By comparing the effectiveness of different variants of the model-based tools, we provide insights on how to design an appropriate input ("prompt") to the model and what influence the size of the model  ...  Our results show that the model-based tools complement (code mutation), are on par (test oracle generation), or even outperform their respective traditionally built tool (test case generation), while imposing  ...  the results of generating test cases with our FSLM-based approach and with Randoop [2] on 18 methods.  ... 
arXiv:2206.01335v2 fatcat:b4eogs5mffa43bi6ny4u5xb364

MetaMorphe

Cesar Torres, Eric Paulos
2015 Proceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition - C&C '15  
multiples of airplane and bird forms reformed and retargeted using a MetaMorphe style.  ...  Through a user study with design experts, MetaMorphe reveals that decisions that physically produce bespoke artifacts or encode unique metadata actively affect perceptions of authorship, agency, and authenticity  ...  ACKNOWLEDGMENTS We thank Jasper O'Leary and Akhila Raju for their aid with user studies; Tim Campbell, Valkyrie Savage, Björn Hartmann, and the anonymous reviewers for their insightful comments.  ... 
doi:10.1145/2757226.2757235 dblp:conf/candc/TorresP15 fatcat:dlwenf4srvhgtdmqjxjg3yrr2a

Bridging the Chasm

Tim Storer
2017 ACM Computing Surveys  
Kanewala and Bieman [2013] also investigated the problem of testing without an oracle and proposed the use of metamorphic testing.  ...  A key finding was that many agile methods can be used successfully in small scale scientific programming teams, with some adaptations.  ... 
doi:10.1145/3084225 fatcat:t2iv3ir5yfamfc7nhz2bg7kuvu

Testing machine learning based systems: a systematic mapping

Vincenzo Riccio, Gunel Jahangirova, Andrea Stocco, Nargiz Humbatova, Michael Weiss, Paolo Tonella
2020 Empirical Software Engineering  
MLS testing is a rapidly growing and developing research area, with many open challenges, such as the generation of realistic inputs and the definition of reliable evaluation metrics and benchmarks.  ...  Context: A Machine Learning based System (MLS) is a software system including one or more components that learn how to perform a task from a given data set.  ...  Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long  ... 
doi:10.1007/s10664-020-09881-0 fatcat:7w42stnm3nfafia7ycbmvhrjou

Grammar Based Directed Testing of Machine Learning Systems [article]

Sakshi Udeshi, Sudipta Chattopadhyay
2019 arXiv   pre-print
We also compare OGMA with a random test generation approach and observe that OGMA is more effective than such random test generation by up to 489%.  ...  We present, to the best of our knowledge, the first approach, which provides a systematic test framework for machine-learning systems that accepts grammar-based inputs.  ...  Search based testing: Search-based testing has a long standing history in the domain of software engineering.  ... 
arXiv:1902.10027v3 fatcat:duzqctcylfatjm3jz35bllh6fy

Extending the space of software test monitoring: practical experience

Mykhailo Lasynskyi, Janusz Sosnowski
2021 IEEE Access  
Software reliability depends on the performed tests. Bug detection and diagnosis are based on test outcome (oracle) analysis.  ...  They focus on assessing test coverage, reasons of low diagnosability, and test result profiles.  ...  Moreover, we can predict the cost of bug repairs basing on historical test results and involved commits (bug handling time, range of code modifications).  ... 
doi:10.1109/access.2021.3136138 fatcat:tlsw6ylvx5e3xod4bf73imkqme
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