1. Software Testing: A Research Travelogue
Alessandro Orso,
Gregg Rothermel

2. Automated Test Input Generation
Symbolic Execution
Search-based testing
Random and fuzzing testing
Combinations

3. Symbolic Execution Static Symbolic Execution General Formula
1970 by King
General Formula …

4. Symbolic Execution Reason of success Increase in computational power
Z3 , Yices , SAT4J
Different variants of symbolic execution
KLEE
DART
CUTE
Pex

5. Symbolic Execution Dynamic Symbolic Execution
2000
DART/CUTE/Pex
Idea of performing symbolic execution that follows a specific path

6. Symbolic Execution Unclear for lasting practical impact
Inherent limitations in the presence of highly structured inputs
Interact with external libraries
Large complex programs
Need for an oracle

7. Symbolic Execution
White-box fuzzing
Sage

8. Search-based Testing
Surveys on use of SBST focusing on Software engineering
Comprehensive analysis and review of trends
Trends, techniques and applications

9. Search-based Testing
Search-based techniques target optimization problems
finding the smallest set of test cases that cover all the branches
Using meta-heuristic search-based optimization techniques

10. Search-based Testing Large range to which it has been applied
Structural testing, model-based testing, mutation testing, temporal testing, exception testing, configuration and interaction testing, stress testing, integration testing etc.
Comprehensive analysis and review of trends

11. Search-based Testing Come in many forms Genetic algorithms
Simulated annealing
Hill climbing
Scatter search
Partical swarm optimization
Tabu search

12. Search-based Testing Challenges and opportunities for SBST Challenges
An orchestrated survey of methodologies for automated software test case generation
Challenges
Oracle
Combining SBST with SE
Opportunities
Co-evolutionary computation, involving multiple populations, possibly under different fitness functions.
Predator-prey relationships
Hyper-heuristic software testing, unite different software activities that utilize SBST

13. Search-based Testing Empirical studies Shortcoming
Search-Based Test Case Generation
Shortcoming
Focusing on unit testing and structural coverage
Limited evidence of applicability for other testing phases and types of coverage

14. Random Testing Improve the random input generation process
Proprotional sampling strategy
JCrasher
Manage the often overwhelmingly large number of test inputs generated
Taming Compiler fuzzers

15. Random Testing One example ART Studies to show the advantages
High overhead and not yet been extended to handle complex input formats
To address problem, techniques are proposed based on mirroring, forgetting, and Voronoi tessellation

16. Random Testing Representative and well-known examples Jcrasher Randoop
Follow up that combines test input generation and static analysis[1][2]
Randoop
Incorporating feedback into the process
De-facto random-testing tool for Java

17. Random Testing Additional approaches Swarm testing Swarm
Attempts to increase the diversity of randomly-generated test inputs

18. Random Testing
Combination with DSE to generate input that can be used to seed symbolic analysis

19. Combined Techniques
Combination of static verification and dynamic verification(i.e., testing)
Yogi project
Testing, under-approximates program behaviors
Static verification, complete but over-approximates program behavior

20. Combined Techniques Combining SE and SBST
Complementary nature of SE and SBST
Using SE as an additional genetic operator[1][2]
Alternating between SE and SBST
Using fitness to select which path to explore in SE
Using symbolic execution to compute fitness values in SBST