Fetching the paper…
Reading the bibliography…
Generating tests that can reveal performance issues in large and complex software systems within a reasonable amount of time is a challenging task.
Active hidden markov models for information extraction
Tobias Scheffer, Christian Decomain, and Stefan Wrobel · 2001
Earlier work this paper cites.
A mathematical theory of communication. acm sigmobile mob
Claude Elwood Shannon · 2001
Earlier work this paper cites.
Specification and implementation of dynamic web site benchmarks
Cristiana Amza, Emmanuel Cecchet, Anupam Chanda, Alan L Cox, Sameh Elnikety, Romer Gil, Julie Marguerite, Karthick Rajamani, and Willy Zwaenepoel · 2002
Earlier work this paper cites.
Model-based performance prediction in software development: A survey
Simonetta Balsamo, Antinisca Di Marco, Paola Inverardi, and Marta Simeoni · 2004
Earlier work this paper cites.
Active learning literature survey
Burr Settles · 2009
Earlier work this paper cites.
Semi-supervised active learning for sequence labeling
Katrin Tomanek and Udo Hahn · 2009
Earlier work this paper cites.
Performance evaluation of component-based software systems: A survey
Heiko Koziolek · 2010
Earlier work this paper cites.
Automatically finding performance problems with feedback-directed learning software testing
Mark Grechanik, Chen Fu, and Qing Xie · 2012
Earlier work this paper cites.
Hybrid active learning for reducing the annotation effort of operators in classification systems
Edwin Lughofer · 2012
Earlier work this paper cites.
Single-pass active learning with conflict and ignorance
Edwin Lughofer · 2012
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
Cited alongside, same era.
Automating performance bottleneck detection using search-based application profiling
Du Shen, Qi Luo, Denys Poshyvanyk, and Mark Grechanik · 2015
Cited alongside, same era.
Generating performance distributions via probabilistic symbolic execution
Bihuan Chen, Yang Liu, and Wei Le · 2016
Cited alongside, same era.
Mining performance regression inducing code changes in evolving software
Qi Luo, Denys Poshyvanyk, and Mark Grechanik · 2016
Cited alongside, same era.
Directed automated memory performance testing
Sudipta Chattopadhyay · 2017
Cited alongside, same era.
Towards holistic continuous software performance assessment
On the evaluation of conditional gans
Terrance DeVries, Adriana Romero, Luis Pineda, Graham W Taylor, and Michal Drozdzal · 2019
Later among the works it cites.
Pyse: Automatic worst-case test generation by reinforcement learning
Jinkyu Koo, Charitha Saumya, Milind Kulkarni, and Saurabh Bagchi · 2019
Later among the works it cites.
Machine learning to guide performance testing: An autonomous test framework
Mahshid Helali Moghadam, Mehrdad Saadatmand, Markus Borg, Markus Bohlin, and Björn Lisper · 2019
Later among the works it cites.
Xstressor: Automatic generation of large-scale worst-case test inputs by inferring path conditions
Charitha Saumya, Jinkyu Koo, Milind Kulkarni, and Saurabh Bagchi · 2019
Later among the works it cites.
Automated model-based performance analysis of software product lines under uncertainty
Paolo Arcaini, Omar Inverso, and Catia Trubiani · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Vincenzo Ferme and Cesare Pautasso · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Cited alongside, same era.
Forepost: Finding performance problems automatically with feedback-directed learning software testing
Qi Luo, Aswathy Nair, Mark Grechanik, and Denys Poshyvanyk · 2017
Cited alongside, same era.
Identifying worst-case user scenarios for performance testing of web applications using markov-chain workload models
Tanwir Ahmad, Dragos Truscan, and Ivan Porres · 2018
Cited alongside, same era.
Perffuzz: Automatically generating pathological inputs
Caroline Lemieux, Rohan Padhye, Koushik Sen, and Dawn Song · 2018
Cited alongside, same era.
Exploratory performance testing using reinforcement learning
Tanwir Ahmad, Adnan Ashraf, Dragos Truscan, and Ivan Porres · 2019
Cited alongside, same era.
Inferring performance from code: a review
Emilio Incerto, Annalisa Napolitano, and Mirco Tribastone · 2020
Later among the works it cites.
The cost of poor quality software in the us: A 2018 report, 2018
Herb Krasner · 2020
Later among the works it cites.
An autonomous performance testing framework using self-adaptive fuzzy reinforcement learning
Mahshid Helali Moghadam, Mehrdad Saadatmand, Markus Borg, Markus Bohlin, and Björn Lisper · 2020
Later among the works it cites.
Automatic exploratory performance testing using a discriminator neural network
Ivan Porres, Tanwir Ahmad, Hergys Rexha, Sébastien Lafond, and Dragos Truscan · 2020
Later among the works it cites.
Study: Software failures cost the enterprise software market $61b annually, 2020
Undo · 2020
Later among the works it cites.