Search Algorithms for Regression Test Case Prioritization
By Zheng Li, Mark Harman, Robert M. Hierons. IEEE Transactions on Software Engineering, 2007
811 citations recorded by the source corpus.
Subfields: Information Systems, Software
View Paper: https://www.semanticscholar.org/paper/d8502cf520fc777a60ccade19ccff1dd228839d6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Mark Harman
- Shin Yoo
- Phil McMinn
- Yuanyuan Zhang
- Hareton K. N. Leung
- Changhai Nie
- S. Afshin Mansouri
- Myra B. Cohen
In Software
- Mark Harman
- Shin Yoo
- Phil McMinn
- Hareton K. N. Leung
- Changhai Nie
- Yuanyuan Zhang
- S. Afshin Mansouri
- Myra B. Cohen
Citations in the corpus
- A survey of combinatorial testing (2011, methodology): “We also need more empirical study to better understand the limitations and strengths of various proposed methods [Li et al. 2007].”
- Constructing Interaction Test Suites for Highly-Configurable Systems in the Presence of Constraints: A Greedy Approach (2008, methodology): “Examples of search activity in software testing include the use of genetic algorithms for test case generation [40], [41], [42], greedy algorithms for regression test case selection and prioritization [43], and our own work that uses simulated annealing for generation of CIT samples [9], [44].”
- Regression testing minimization, selection and prioritization: a survey (2012, methodology): “applied various meta-heuristics for test case prioritization [128].”
- Search Based Software Engineering: A Comprehensive Analysis and Review of Trends Techniques and Applications (2009, methodology): “Traditionally, greedy algorithms have been used for minimization and prioritization and a range of techniques involving static analysis have been applied to all three problems [49, 302, 329, 331, 499].”
- Search-Based Software Testing: Past, Present and Future (2011, methodology): “Search-based optimisation has been used as an enabler to a plethora of testing problems, including functional testing [6], [7], temporal testing [8], [9], [10], integration testing [11], regression testing [12], stress testing [13], mutation testing [14], test prioritisation [15], [16], interaction ”