software testing

One of the most important, but tedious and costly tasks of the software testing process is test data generation. The challenge is finding approaches in which humans could generate test data through more attractive, faster, and cheaper ways. One approach is using Game with A Purpose in the process of test data generation. In our previous work, we introduced two games called Rings and Greenify, by which many inexpensive players, with no special technical abilities, become engaged in test data generation. Despite the promising results of Rings and Greenify, they have certain limitations and issues. In this paper, we present a new GWAP for test data generation, called QOTE, in order to improve the application of GWAP in test data generation for program units. QOTE provides a different game-play and has certain advantages compared to prior gamesc. Experimental results have shown that QOTE outperforms prior games from two aspects: game quality and capability of test data generation. We have conducted another experiment based on mutation analysis to further evaluate test data generation capabilities of QOTE compared to four automatic approaches and show that the test data generated by QOTE can reveal more failures compared to the mentioned automatic approaches.

Using machine learning techniques for constructing automated test oracles have been successful in recent years. However, existing machine learning based oracles have deficiencies when applied to software systems with low observability, such as embedded software, cyber-physical systems, multimedia software programs, and computer games. This paper proposes a new black box approach to construct automated oracles which can be applied to software systems with low observability. The proposed approach employs an Artificial Neural Network (ANN) algorithm which uses input values as well as corresponding pass/fail outcomes of the program under test, as the training set. To evaluate the performance of the proposed approach, we have conducted extensive experiments on several benchmarks. The results manifest the applicability of the proposed approach to software systems with low observability as well as its higher accuracy in comparison to a well-known machine learning based method. We have also assessed the effect of different parameters on the accuracy of the proposed approach.

Let G be a weighted digraph and s and t be two vertices of G. The reachability assurance (RA) problem is how to label the edges of G such that every path starting at s finally reaches t and the sum of the weights of the labeled edges, called the RA cost, is minimal. The common approach to the RA problem is pathfinding, in which a path is sought from s to t and then the edges of the path are labeled. This paper introduces a new approach, the marking problem (MP), to the RA problem. Compared to the common pathfinding approach, the proposed MP approach has a lower RA cost. It is shown that the MP is NP-complete, even when the underlying digraph is an unweighted directed acyclic graph (DAG) or a weighted DAG with an out-degree of two. An appropriate heuristic algorithm to solve the MP in polynomial time is provided. To mitigate the RA problem as a serious challenge in this area, application of the MP in software testing is also presented. By evaluating the datasets from various program flow graphs, it is shown that the MP is superior to the pathfinding in the context of test case generation.

Let G be a weighted digraph and s and t be two vertices of G. The reachability assurance (RA) problem is how to label the edges of G such that every path starting at s finally reaches t and the sum of the weights of the labeled edges, called the RA cost, is minimal. The common approach to the RA problem is pathfinding, in which a path is sought from s to t and then the edges of the path are labeled. This paper introduces a new approach, the marking problem (MP), to the RA problem. Compared to the common pathfinding approach, the proposed MP approach has a lower RA cost. It is shown that the MP is NP-complete, even when the underlying digraph is an unweighted directed acyclic graph (DAG) or a weighted DAG with an out-degree of two. An appropriate heuristic algorithm to solve the MP in polynomial time is provided. To mitigate the RA problem as a serious challenge in this area, application of the MP in software testing is also presented. By evaluating the datasets from various program flow graphs, it is shown that the MP is superior to the pathfinding in the context of test case generation.

Search-based optimization methods have been used for software engineering activities such as software testing. In the field of software testing, search-based test data generation refers to application of meta-heuristic optimization methods to generate test data that cover the code space of a program. Automatic test data generation that can cover all the paths of software is known as a major challenge.
The paper establishes a new cost function for automatic test data generation, which can traverse the non-iterative paths of software control flow graphs. This function is later compared with similar cost functions proposed in other articles. The results indicate the superior performance of the proposed function. Still another innovation in this paper is the application of the Imperialist Competitive Algorithm in automatic test data generation along with the proposed cost function. Automatic test data generation is implemented through the Imperialist Competitive Algorithm as well as the Genetic and Particle Swarm Optimization Algorithms for three software programs with different search space sizes. The algorithms are compared with each other in terms of convergence speed, computational time, and local search. Test data generated by the proposed method has achieved better results than other algorithms in finding the number of non-iterative paths, the convergence speed and computational time with growing the searching space of the software's control flow graph.

Systems of computers and their application in the lives of modern human beings are vastly expanding. In any kind of computer application، failure in computer systems can lead to a range of financial and mortal losses. Indeed، the major origin of software failure can be located in designing or implementing software. With regard to these statistics، 30% of the software projects have been prosperous and successful. The proposed method is intended to reduce the cost and time of testing and it focuses on enhancing the efficiency of software testing methods. In this paper، we investigated the effect of slicing techniques on the reduction rate of testing cost and time. The results of experiments show that we can cover a large number of program instructions، branches and paths by a small number of test cases in the sliced program.