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Specification inference techniques aim at (automatically) inferring a set of assertions that capture the exhibited software behaviour by generating and filtering assertions through dynamic test executions and mutation testing.
Object-Oriented Software Construction, 2nd Edition
Bertrand Meyer · 1997
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Random forests
Leo Breiman · 2001
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How the design of JML accommodates both runtime assertion checking and formal verification
Gary T. Leavens, Yoonsik Cheon, Curtis Clifton, Clyde Ruby, and David R. Cok · 2005
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A historical perspective on runtime assertion checking in software development
Lori A. Clarke and David S. Rosenblum · 2006
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An empirical comparison of automated generation and classification techniques for object-oriented unit testing
Marcelo d’Amorim, Carlos Pacheco, Tao Xie, Darko Marinov, and Michael D. Ernst · 2006
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Inference and enforcement of data structure consistency specifications
Brian Demsky, Michael D. Ernst, Philip J. Guo, Stephen McCamant, Jeff H. Perkins, and Martin C. Rinard · 2006
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The daikon system for dynamic detection of likely invariants
Michael D. Ernst, Jeff H. Perkins, Philip J. Guo, Stephen McCamant, Carlos Pacheco, Matthew S. Tschantz, and Chen Xiao · 2007
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Feedback-directed random test generation
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Introduction to Software Testing
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Pex-white box test generation for .net
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Automatically patching errors in deployed software
Jeff H. Perkins, Sunghun Kim, Samuel Larsen, Saman P. Amarasinghe, Jonathan Bachrach, Michael Carbin, Carlos Pacheco, Frank Sherwood, Stelios Sidiroglou, Gregory T. Sullivan, Weng-Fai Wong, Yoav Zibin, Michael D. Ernst, and Martin C. Rinard · 2009
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Analysis of invariants for efficient bounded verification
Juan P. Galeotti, Nicolás Rosner, Carlos López Pombo, and Marcelo F. Frias · 2010
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Evaluating mutation testing alternatives: A collateral experiment
Marinos Kintis, Mike Papadakis, and Nicos Malevris · 2010
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Is operator-based mutant selection superior to random mutant selection?
Lu Zhang, Shan-Shan Hou, Jun-Jue Hu, Tao Xie, and Hong Mei · 2010
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Evosuite: automatic test suite generation for object-oriented software
Gordon Fraser and Andrea Arcuri · 2011
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MAJOR: an efficient and extensible tool for mutation analysis in a java compiler
René Just, Franz Schweiggert, and Gregory M. Kapfhammer · 2011
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Mutation-driven generation of unit tests and oracles
Gordon Fraser and Andreas Zeller · 2012
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Modular and verified automatic program repair
Francesco Logozzo and Thomas Ball · 2012
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Recurrent continuous translation models
Nal Kalchbrenner and Phil Blunsom · 2013
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Establishing theoretical minimal sets of mutants
Paul Ammann, Márcio Eduardo Delamaro, and Jeff Offutt · 2014
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Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Researcher bias: The use of machine learning in software defect prediction
Martin J. Shepperd, David Bowes, and Tracy Hall · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
Time to clean your test objectives
Michaël Marcozzi, Sébastien Bardin, Nikolai Kosmatov, Mike Papadakis, Virgile Prevosto, and Loïc Correnson · 2018
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code2seq: Generating sequences from structured representations of code
Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav · 2019
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The importance of accounting for real-world labelling when predicting software vulnerabilities
Matthieu Jimenez, Renaud Rwemalika, Mike Papadakis, Federica Sarro, Yves Le Traon, and Mark Harman · 2019
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Chapter six - mutation testing advances: An analysis and survey
Mike Papadakis, Marinos Kintis, Jie Zhang, Yue Jia, Yves Le Traon, and Mark Harman · 2019
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Performance evaluation of deep neural networks applied to speech recognition: Rnn, LSTM and GRU
Apeksha Shewalkar, Deepika Nyavanandi, and Simone A. Ludwig · 2019
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Neural machine translation by jointly learning to align and translate
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The oracle problem in software testing: A survey
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
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End-to-end attention-based large vocabulary speech recognition
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On the limits of mutation reduction strategies
Rahul Gopinath, Mohammad Amin Alipour, Iftekhar Ahmed, Carlos Jensen, and Alex Groce · 2016
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Analyzing the validity of selective mutation with dominator mutants
Bob Kurtz, Paul Ammann, Jeff Offutt, Márcio Eduardo Delamaro, Mariet Kurtz, and Nida Gökçe · 2016
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Michele Tufano, Jevgenija Pantiuchina, Cody Watson, Gabriele Bavota, and Denys Poshyvanyk · 2019
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An empirical study on learning bug-fixing patches in the wild via neural machine translation
Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk · 2019
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Learning how to mutate source code from bug-fixes
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Selecting fault revealing mutants
Thierry Titcheu Chekam, Mike Papadakis, Tegawendé F. Bissyandé, Yves Le Traon, and Koushik Sen · 2020
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What is the vocabulary of flaky tests?
Gustavo Pinto, Breno Miranda, Supun Dissanayake, Marcelo d’Amorim, Christoph Treude, and Antonia Bertolino · 2020
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Evolutionary improvement of assertion oracles
Valerio Terragni, Gunel Jahangirova, Paolo Tonella, and Mauro Pezzè · 2020
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On learning meaningful assert statements for unit test cases
Cody Watson, Michele Tufano, Kevin Moran, Gabriele Bavota, and Denys Poshyvanyk · 2020
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Assessing software defection prediction performance: why using the matthews correlation coefficient matters
Jingxiu Yao and Martin J. Shepperd · 2020
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Evospex: An evolutionary algorithm for learning postconditions
Facundo Molina, Pablo Ponzio, Nazareno Aguirre, and Marcelo F. Frias · 2021
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Learning from what we know: How to perform vulnerability prediction using noisy historical data
Aayush Garg, Renzo Degiovanni, Matthieu Jimenez, Maxime Cordy, Mike Papadakis, and Yves Le Traon · 2022
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Cerebro: Static subsuming mutant selection
Aayush Garg, Milos Ojdanic, Renzo Degiovanni, Thierry Titcheu Chekam, Mike Papadakis, and Yves Le Traon · 2022
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Fuzzing class specifications
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