Generating Software Test Data by Evolution

By Christoph C. Michael, Gary E. McGraw Jr., Michael Daniel Schatz. IEEE Trans. Software Eng., 2001

545 citations recorded by the source corpus.

Subfields: Computer Science Applications, Software

View Paper: https://www.semanticscholar.org/paper/d96909feaf1a4655c4c2f28c1e80748016a2cca4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Yue Jia
2. Mark Harman
3. Earl T. Barr
4. Thanhvu Nguyen
5. Claire Le Goues
6. Stephanie Forrest
7. Phil McMinn
8. Westley Weimer

In Software

1. Mark Harman
2. Claire Le Goues
3. Yue Jia
4. Westley Weimer
5. Stephanie Forrest
6. Yuriy Brun
7. Edward K. Smith
8. Earl T. Barr

Citations in the corpus

• Search Based Software Engineering: A Comprehensive Analysis and Review of Trends Techniques and Applications (2009, unknown): “…of branch coverage [9] [26] [27] [30] [70] [86] [87] [108] [158] [179] [192] [240] [260] [261] [263] [271] [286] [289] [297] [306] [307] [311] [312] [339] [344] [349] [350] [351] [352] [353] [354] [356] [377] [380] [382] [388] [399] [400] [401] [440] [441] [442] [444] [445] [463] [465] [466] [475]…”
• A Theoretical and Empirical Study of Search-Based Testing: Local, Global, and Hybrid Search (2010, methodology): “This work has been extended by various authors [ 39 ], [51], [55], [57] for branch coverage.”
• Is the cure worse than the disease? overfitting in automated program repair (2015, methodology): “Search-based software engineering has been used for developing test suites [40,59], finding safety violations [3], refactoring [53], and project management and effort estimation [8].”
• Machine Learning and Software Engineering (2002, unknown): “A tool is reported in [65, 66] that uses, among other things, GA to generate dynamic test data for C/C++ programs.”
• An orchestrated survey of methodologies for automated software test case generation (2013, methodology): “SBST has been applied to a wide variety of testing goals including structural (Harman and McMinn, 2010; McMinn et al., 2012; Michael et al., 2001; Tonella, 2004), functional (Wegener and Bühler, 2004), non-functional (Wegener and Grochtmann, 1998) and state-based”
• Search‐based software test data generation: a survey (2004, background): “[45] alleviate this problem for condition coverage, by delaying an attempt to satisfy a condition within a branching expression until previous individuals have been already found which reach the branching node in question.”
• GenProg: A Generic Method for Automatic Software Repair (2012, background): “, to develop test suites [70], [71], [72].”
• Higher Order Mutation Testing (2009, methodology): “This version is the one used by Michael and McGraw in their test data generation study [18].”
• Software Testing (2002, unknown): “However, this type of symbolic generation has a limited practical value due to the problems associated with iteration constructs and arrays that depends upon other program variables and pointers [Michael et al., 2001].”
• Automatically finding patches using genetic programming (2009, methodology): “The field of Search-Based Software Engineering (SBSE) [20] uses evolutionary and related methods for software testing, e.g., to develop test suites [ 24 , 32, 33].”