Automatic test path generation from sequence diagram using genetic algorithm

Abstract

Software testing is an important and complicated phase of software development cycle. Software test process acquires test cases as input for the system under test to evaluate the behavior of the product. If test cases are prepared before coding, it will help the developers to control their code to conform to specification. White box testing requires a set of predefined test paths to generate test cases, therefore generating a set of reliable test paths is a critical task. The most common approach in white box testing is to generate test paths from source code while the generation process must be delayed until completion of source code. Using sequence diagram as an input artifact for generating test path is cost and time efficient due to the fact that test process starts before implementation phase. Furthermore, tester involvement in source code complexity is reduced to a minimum. Test paths are generated from the control flow graph, which is extracted from sequence diagrams. Among all graph based coverage criteria, Prime path coverage subsumes different graph based coverage criteria that lead us to complete path coverage. Also, prime path coverage concentrates on visiting all nodes and edges in the control flow graph rather than traversing all existing paths, which results in test effort reduction. Genetic algorithm is applied minimize the number of test cases required to reach the highest coverage.In this paper, we proposed a model to generate all prime paths automatically and extract minimum paths with shortest possible length, which covers all prime paths by means of genetic algorithm. The experimental results show the generated paths can easily turn into optimal test paths with the best prime path coverage having the least number of test paths.

Employment of multiple algorithms for optimal path-based test selection strategy

“…To generate path-based test cases, a number of algorithms have been proposed [2], [3], [4], [5], [6], [7], [15], such as the Brute Force algorithm, the Set-Covering Based Solution, or the Matching-Based Prefix Graph Solution [3]. Additionally, genetic algorithms have been employed to generate the prime paths [6] or to generate basis test paths [16]. Other natureinspired algorithms have also been proposed, for example, ant colony optimization algorithms [5], [17], the firefly algorithm [18] and algorithms inspired by microbiology [4].…”

“…To generate the path-based test cases systematically and consistently, a SUT model based on a directed graph is used [1]. Several algorithms have been presented (e.g., [2], [3], [4], [5], [6], [7]) to solve this problem. However, based on both M. Bures evidence from the literature and our experiments while developing new algorithms to solve this problem, it is challenging task to find a universal algorithm that can generate an optimal test set for all instances.…”

Use of Firefly Algorithm in Optimization and Prioritization of Test Paths Generated from UML Sequence Diagram

“…Table 5 shows test paths generated from Firefly algorithm and test paths prioritization values are calculated from the mean of the brightness values on each node of MFG. Table 6 shows the paths generated from proposed approach on case study taken by [29]. Authors also generated test paths from Genetic algorithm based approach.…”

Automated Test Path Generation using Genetic Algorithm

“…1 shows the types of software testing. Path testing is an approach to testing which ensures that every path through a program has been executed at least once [1]. The starting point for path testing is a program flow graph.…”

“…RELATED WORKS Bahare Hoseini and Saeed Jalili [1] have proposed a model to generate test paths from UML sequence diagram using genetic algorithm. This model achieves prime path coverage, which is the strongest graph based coverage criteria.…”