The document describes Nighthawk, a tool that uses a two-level genetic algorithm approach to generate randomized unit test data. At the randomized testing level, it selects methods and arguments randomly to generate test cases. At the GA level, it evolves test generation parameters to increase code coverage. An empirical evaluation on Java collection classes found that enriched test wrappers and deep target analysis led to higher coverage than normal approaches. The GA was able to find effective parameter settings and achieve similar or better coverage than previous random and heuristic search techniques.

1. Nighthawk: A Two-Level Genetic-Random Unit Test Data Generator Jamie Andrews and Felix C. H. Li Department of Computer Science University of Western Ontario Tim Menzies Lane Department of Computer Science West Virginia University

15. Value Pools and Methods TreeMap int Employee . . . . . . . . . . . . . . . . . . . . . . ... t.put(e, i); "value reuse policy"

18. Initialization

19. Cloning

20. Mutation, Recombination

21. Fitness Evaluation 4324 4300 4288 3696 3559 3331 3278 3277 3000

22. Sorting 4300 4288 3696 3559 3278 3000 4324 3331 3277

23. Retention 4300 4288 4324

24. Fitness Function (number of lines covered) * 1000 - (number of method calls) brake on test case length reward for high coverage

28. Results – Coverage (Lines) Enriched test wrappers Deep target analysis Both ... 325 (.92) 252 253 205 355 Hashtable 44 (.96) 26 40 24 46 HashSet 347 (.96) 305 265 238 360 HashMap 7 (.03) 10 9 7 239 EnumMap 140 (.93) 109 140 111 150 ArrayList ED PD EN PN SLOC Source

29. Results – Time (Clock Sec.) ... 157 110 110 8 Hashtable 39 27 29 25 HashSet 176 136 37 63 HashMap 5 6 9 3 EnumMap 48 29 91 75 ArrayList ED PD EN PN Source

33. Thank you!