Yesterday, my program worked. Today, it does not. Why?
ESEC/FSE-7 Proceedings of the 7th European software engineering conference held jointly with the 7th ACM SIGSOFT international symposium on Foundations of software engineering
The ant colony optimization meta-heuristic
New ideas in optimization
Ant algorithms for discrete optimization
Artificial Life
Automatic predicate abstraction of C programs
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
Automatically validating temporal safety properties of interfaces
SPIN '01 Proceedings of the 8th international SPIN workshop on Model checking of software
A framework for multi-valued reasoning over inconsistent viewpoints
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
The SLAM project: debugging system software via static analysis
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Isolating cause-effect chains from computer programs
Proceedings of the 10th ACM SIGSOFT symposium on Foundations of software engineering
From symptom to cause: localizing errors in counterexample traces
POPL '03 Proceedings of the 30th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
An Approach to Compositional Model Checking
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
An ant colony optimization approach to the software release planning with dependent requirements
SSBSE'11 Proceedings of the Third international conference on Search based software engineering
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
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In this paper, we propose a new approach of software error trace and model checking, incorporating ant colony based agents and self regulated swarms. The automated testing already becomes popular to identify difference of program states in a given piece of source code and thus we focus particularly in the transition of program states, which is effectively monitored by pheromone deposition by these ants and swarms. Finally, an algorithm is developed and implemented contemplating this idea of automated software test and model checking. The advantage of this proposal is to generate multiple error trace having independent root cause, easily traced by labeled path in control dependence graph evolved from the program to be tested. The pheromone distribution of ant and swarms, in the different proportion across this graph signal the error trace in caller and callee function if they mismatch in the program and could display the pheromone value of the path where software error has been localized (root cause). The results are also presented on this new approach of software testing.