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Search-Based Software Engineering: 11th International Symposium, SSBSE 2019, Tallinn, Estonia, August 31 - September 1, 2019, Proceedings

1st Edition 2019, 2019

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Generating Effective Test Suites by Combining Coverage Criteria

Search Based Software Engineering, p.65-82

Gay, Gregory

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Implications of ceiling effects in defect predictors

International Conference on Software Engineering 2008, 2008, Vol.2008 (16), p.47-54

2008

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Improving the Readability of Generated Tests Using GPT-4 and ChatGPT Code Interpreter

Context-Infused Automated Software Test Generation, 2024, Vol.14415 LNCS, p.140-146

Gay, Gregory

2024

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The integration of machine learning into automated test generation: A systematic mapping study

Software testing, verification & reliability, 2023-06, Vol.33 (4), p.n/a

2023

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Special Section on the 2019 Symposium on Search-Based Software Engineering

Information and software technology, 2021-02, Vol.130, p.106440, Article 106440

2021

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Automated oracle creation support, or: how I learned to stop worrying about fault propagation and love mutation testing

2012 34th International Conference on Software Engineering (ICSE), 2012, p.870-880

2012

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Mapping the structure and evolution of software testing research over the past three decades

The Journal of systems and software, 2023-01, Vol.195, p.111518, Article 111518

2023

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Non-functional requirements for machine learning: understanding current use and challenges among practitioners

Requirements engineering, 2023, Vol.28 (2), p.283-316

2023

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To Call, or Not to Call: Contrasting Direct and Indirect Branch Coverage in Test Generation

2018 IEEE/ACM 11th International Workshop on Search-Based Software Testing (SBST), 2018, p.43-50

Gay, Gregory

2018

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Ensuring the Observability of Structural Test Obligations

IEEE transactions on software engineering, 2020-07, Vol.46 (7), p.748-772

2020

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Choosing the fitness function for the job: Automated generation of test suites that detect real faults

Software testing, verification & reliability, 2019-06, Vol.29 (4-5), p.n/a

2019

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Multifaceted Test Suite Generation Using Primary and Supporting Fitness Functions

2018 IEEE/ACM 11th International Workshop on Search-Based Software Testing (SBST), 2018, p.2-5

Gay, Gregory

2018

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Detecting Real Faults in the Gson Library Through Search-Based Unit Test Generation

Search-Based Software Engineering, p.385-391

Gay, Gregory

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Automatically finding the control variables for complex system behavior

Automated software engineering, 2010-12, Vol.17 (4), p.439-468

2010

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Testing, Debugging, and Log Analysis With Modern AI Tools

IEEE software, 2024-03, Vol.41 (2), p.99

2024

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Defects4J as a Challenge Case for the Search-Based Software Engineering Community

12th International Symposium on Search-Based Software Engineering, SSBSE 2020, Bari, Italy, 2020, Vol.12420 LNCS, p.255-261

2020

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Bytecode-Based Multiple Condition Coverage: An Initial Investigation

Search-Based Software Engineering, 2020, Vol.SSBSE 2020, p.220-236

2020

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Learning how to search: generating effective test cases through adaptive fitness function selection

Empirical software engineering : an international journal, 2022-03, Vol.27 (2), Article 38

2022

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Developer Views on Software Carbon Footprint and Its Potential for Automated Reduction

Search-Based Software Engineering, p.35-51

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Exploring Genetic Improvement of the Carbon Footprint of Web Pages

15th International Symposium on Search-Based Software Engineering, SSBSE 2023, San Francisco, USA, 2024, Vol.14415 LNCS, p.67-83

2024

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The Risks of Coverage-Directed Test Case Generation

IEEE transactions on software engineering, 2015-08, Vol.41 (8), p.803-819

2015

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Using Search-Based Test Generation to Discover Real Faults in Guava

Search Based Software Engineering, p.153-160

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Generating Diverse Test Suites for Gson Through Adaptive Fitness Function Selection

Search-Based Software Engineering, 2020, Vol.SSBSE 2020, p.246-252

2020

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Choosing the fitness function for the job: Automated generation of test suites that detect real faults

Software testing, verification & reliability, 2020-11, Vol.30 (7-8), p.n/a

2020

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Search-Based Software Engineering: 11th International Symposium, SSBSE 2019, Tallinn, Estonia, August 31 - September 1, 2019, Proceedings

Generating Effective Test Suites by Combining Coverage Criteria

Implications of ceiling effects in defect predictors

Improving the Readability of Generated Tests Using GPT-4 and ChatGPT Code Interpreter

The integration of machine learning into automated test generation: A systematic mapping study

Special Section on the 2019 Symposium on Search-Based Software Engineering

Automated oracle creation support, or: how I learned to stop worrying about fault propagation and love mutation testing

Mapping the structure and evolution of software testing research over the past three decades

Non-functional requirements for machine learning: understanding current use and challenges among practitioners

To Call, or Not to Call: Contrasting Direct and Indirect Branch Coverage in Test Generation

Ensuring the Observability of Structural Test Obligations

Choosing the fitness function for the job: Automated generation of test suites that detect real faults

Multifaceted Test Suite Generation Using Primary and Supporting Fitness Functions

Detecting Real Faults in the Gson Library Through Search-Based Unit Test Generation

Automatically finding the control variables for complex system behavior

Testing, Debugging, and Log Analysis With Modern AI Tools

Defects4J as a Challenge Case for the Search-Based Software Engineering Community

Bytecode-Based Multiple Condition Coverage: An Initial Investigation

Learning how to search: generating effective test cases through adaptive fitness function selection

Developer Views on Software Carbon Footprint and Its Potential for Automated Reduction

Exploring Genetic Improvement of the Carbon Footprint of Web Pages

The Risks of Coverage-Directed Test Case Generation

Using Search-Based Test Generation to Discover Real Faults in Guava

Generating Diverse Test Suites for Gson Through Adaptive Fitness Function Selection

Choosing the fitness function for the job: Automated generation of test suites that detect real faults

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