Test Data Generation using Artificial Life

2014

Self Cite

Manual test data generation is carried out by using the ability of neurons to recognize patterns. The nervous system and the brain coordinate to generate test cases, which are capable of finding potential faults. Automated test data generators lack the ability to produce efficient test cases because they do not imitate natural processes. This paper proposes using Artificial Life based systems for generating test cases. Cellular Automata and Langton's loop have been used to accomplish the above task. Cellular Automata are parallel distributed systems capable of reproducing using self generated patterns. These fascinating techniques have been amalgamated with standard test data generation techniques to give rise to a methodology, which generates test cases for white box testing. Langton's Loops have been used to generate test cases for Black Box Testing. The approach has been verified on a set of 20 programs. The programs have been selected on the basis of their Lines of Code and utility. The results obtained have been verified using Average Probability of Fault Detection. This paper also proposes a new framework capable of crafting test cases taking into account the oracle cost.

Section: Introductionmentioning

confidence: 80%

“…It may be stated here that the use of AL in test data generation has not been completely explored. Moreover, except for one attempt, which uses Langton's loop [7,8], no other work was found on the application of AL to test data generation.…”

Section: Figure 1: Automated Test Data Generatormentioning

confidence: 96%

Artificial life and cellular automata based automated test case generator

2014

Self Cite

“…The flow of software is determined by the order in which modules call each other. This essence is captured in the Module State Diagram (MSD), which was proposed in one of our previous works [25]. The Module State Diagram can be defined as follows.…”

Section: Module State Diagram Generatormentioning

confidence: 99%

Neural network based black box testing

Khanna²

2014

Self Cite

Black Box Testing is immensely important because the source code of a module is not always available. Enterprise Resource Planning systems are also tested using Black Box Testing wherein all the test cases are not equally important. The prioritization of these test cases would be helpful in case of premature termination of testing, due to lack of resources. This paper proposes a Neural Network based method to prioritize test cases. The paper also presents guidelines for prioritizing test cases. The technique has been tested using a financial management system and the results are encouraging. This paper paves way for applying Neural Network in Black Box Testing and presents a framework, which would help both researchers and practitioners.

“…The work is a part of a larger system being developed. The second component of the system is based on the concept of Lagton's Loop [3]. The component produces test cases on the basis of the signature of a method.…”

Section: Conclusion and Future Scopementioning

confidence: 99%

Cellular-genetic test data generation

Singla

2013

Self Cite

Test Data Generation is the soul of automated testing. The dream of having efficient and robust automated testing software can be fulfilled only if the task of designing a robust automated test data generator can be accomplished. In the work we explore the gaps in the existing techniques and intend to fill these gaps by proposing new algorithms. The following work presents algorithms that handle almost all the constructs of procedural programming languages. The proposed technique uses cellular automata as its base. The use of Cellular Automata brings a blend of artificial life to the work. The work is a continuation of our earlier attempt to amalgamate Cellular Automata based algorithms to generate test data. The technique has been applied to C programs and is currently being tested on a financial enterprise resource planning system. Since, the solution of most of the problems can be found by observing nature, we must explore artificial nature to accomplish the above task.