Elements of information theory
Elements of information theory
STATEMATE applied to statistical software testing
ISSTA '93 Proceedings of the 1993 ACM SIGSOFT international symposium on Software testing and analysis
The craft of software testing: subsystem testing including object-based and object-oriented testing
The craft of software testing: subsystem testing including object-based and object-oriented testing
The chaining approach for software test data generation
ACM Transactions on Software Engineering and Methodology (TOSEM)
Assertion-oriented automated test data generation
Proceedings of the 18th international conference on Software engineering
A tight analysis of the greedy algorithm for set cover
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Code generation and analysis for the functional verification of micro processors
DAC '96 Proceedings of the 33rd annual Design Automation Conference
A safe, efficient regression test selection technique
ACM Transactions on Software Engineering and Methodology (TOSEM)
Automated test data generation using an iterative relaxation method
SIGSOFT '98/FSE-6 Proceedings of the 6th ACM SIGSOFT international symposium on Foundations of software engineering
An empirical study of regression test selection techniques
Proceedings of the 20th international conference on Software engineering
Automated test-data generation for exception conditions
Software—Practice & Experience
Regression test selection for Java software
OOPSLA '01 Proceedings of the 16th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Test Case Prioritization: A Family of Empirical Studies
IEEE Transactions on Software Engineering
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
An Informal Formal Method for Systematic JUnit Test Case Generation
Proceedings of the Second XP Universe and First Agile Universe Conference on Extreme Programming and Agile Methods - XP/Agile Universe 2002
Coverage directed test generation for functional verification using bayesian networks
Proceedings of the 40th annual Design Automation Conference
Whole program Path-Based dynamic impact analysis
Proceedings of the 25th International Conference on Software Engineering
Compacting regression-suites on-the-fly
APSEC '97 Proceedings of the Fourth Asia-Pacific Software Engineering and International Computer Science Conference
Genetic algorithms for dynamic test data generation
ASE '97 Proceedings of the 12th international conference on Automated software engineering (formerly: KBSE)
Evolving Complex Othello Strategies Using Marker-Based Genetic Encoding ofNeural Networks
Evolving Complex Othello Strategies Using Marker-Based Genetic Encoding ofNeural Networks
Probabilistic regression suites for functional verification
Proceedings of the 41st annual Design Automation Conference
Multithreaded java program test generation
IBM Systems Journal
Reaching coverage closure in post-silicon validation
HVC'10 Proceedings of the 6th international conference on Hardware and software: verification and testing
Leveraging accelerated simulation for floating-point regression
HVC'12 Proceedings of the 8th international conference on Hardware and Software: verification and testing
Hi-index | 5.23 |
Automated regression suites are essential in developing large applications, while maintaining reasonable quality and timetables. The main argument against the automation of regression suites, in addition to the cost of creation and maintenance, is the observation that if you run the same test many times, it becomes increasingly less likely to find bugs. To alleviate such problems, a new regression suite practice, using random test generators to create regression suites on-the-fly, is becoming more common. In this practice, instead of maintaining tests, we generate test suites on-the-fly by choosing several specifications and generating a number of tests from each specification. We describe techniques for optimizing random generated test suites. We first show how the set cover greedy algorithms, commonly used for selecting tests for regression suites, may be adapted to selecting specifications for randomly generated regression suites. We then introduce a new class of greedy algorithms, referred to as future-aware greedy algorithms. The algorithms are computationally efficient and generate more effective regression suites.