Praveen Ranjan Srivastava

An approach of optimal path generation using ant colony optimization

Software Testing is one of the indispensable parts of the software development lifecycle and structural testing is one of the most widely used testing paradigms to test various software. Structural testing relies on code path identification, which in turn leads to identification of effective paths. Aim of the current paper is to present a simple and novel algorithm with the help of an ant colony optimization, for the optimal path identification by using the basic property and behavior of the ants. This novel approach uses certain set of rules to find out all the effective/optimal paths via ant colony optimization (ACO) principle. The method concentrates on generation of paths, equal to the cyclomatic complexity. This algorithm guarantees full path coverage.

Automated Software Testing Using Metahurestic Technique Based on an Ant Colony Optimization

Software testing is an important and valuable part of the software development life cycle. Due to time, cost and other circumstances, exhaustive testing is not feasible that’s why there is a need to automate the testing process. Testing effectiveness can be achieved by the State Transition Testing (STT) which is commonly used in real time, embedded and web-based kinds of software systems. The tester’s main job to test all the possible transitions in the system. This paper proposed an Ant Colony Optimization (ACO) technique for the automated and full coverage of all state-transitions in the system. Present paper approach generates test sequence in order to obtain the complete software coverage. This paper also discusses the comparison between two metaheuristic techniques (Genetic Algorithm and Ant Colony optimization) for transition based testing.

Generation of test data using meta heuristic approach

Software testing is of huge importance to development of any software. The prime focus is to minimize the expenses on the testing. In software testing the major problem is generation of test data. Several metaheuristic approaches in this field have become very popular. The aim is to generate the optimum set of test data, which would still not compromise on exhaustive testing of software. Our objective is to generate such efficient test data using genetic algorithm and ant colony optimization for a given software. We have also compared the two approaches of software testing to determine which of these are effective towards generation of test data and constraints if any.

Test Case Optimization Using Artificial Bee Colony Algorithm

Software Testing is one of the integral parts of software development lifecycle. Exhaustive testing on software is impossible to achieve as the testing is a continuous process. Using this as a constraint, software testing is performed in a way that requires reducing the testing effort but should provide high quality software that can yield comparable results. To accomplish this optimized testing, a software test case optimization technique based on artificial bee colony algorithm is proposed here. Based on intelligent behavior of honey bee, this method generates optimal number of test cases to be executed on software under test (SUT). This approach is qualified by self-organization, robustness and focuses on generation of paths derived from cyclomatic complexity. The resulting solution guarantees full path coverage.

Integrated Software Quality Evaluation: A Fuzzy Multi-Criteria Approach

Software measurement is a key factor in managing, controlling, and improving the software development processes. Software quality is one of the most important factors for assessing the global competitive position of any software company. Thus the quantification of quality parameters and integrating them into quality models is very essential. Software quality criteria are not very easily measured and quantified. Many attempts have been made to exactly quantify the software quality parameters using various models such as ISO/IEC 9126 Quality Model, Boehms Model, McCalls model, etc. In this paper an attempt has been made to provide a tool for precisely quantifying software quality factors with the help of quality factors stated in ISO/IEC 9126 model. Due to the unpredictable nature of the software quality attributes, the fuzzy multi criteria approach has been used to evolve the quality of the software.

Automated Test Data Generation Using Cuckoo Search and Tabu Search (CSTS) Algorithm

Abstract. Software testing is a very important phase in the development of software. Testing includes the generation of test cases which, if done manually, is time consuming. To automate this process and generate optimal test cases, several meta-heuristic techniques have been developed. These approaches include genetic algorithm, cuckoo search, tabu search, intelligent water drop, etc. This paper presents an effective approach for test data generation using the cuckoo search and tabu search algorithms (CSTS). It combines the cuckoo algorithms strength of converging to the solution in minimal time along with the tabu mechanism of backtracking from local optima by Lévy flight. The experimental results show that the algorithm is effective in generating test cases optimally and its performance is better than various earlier proposed approaches.

Application of genetic algorithm and tabu search in software testing

This paper presents a technique for automatic test-data generation in software testing. The proposed approach is based on genetic and tabu search algorithms. It combines the strength of two metaheuristic techniques and produces efficient results. The conventional approach for test-data generation using genetic algorithm is modified by applying a tabu search heuristic in mutation step. It also incorporates backtracking process to move search away from local optima. The experimental results show that the algorithm is effective in providing test data and its performance is better than simple genetic algorithm.

Use of genetic algorithm in generation of feasible test data

In recent years researchers have applied the concept of Genetic Algorithm in generation of test data for effective software testing. Several attempts have been made to develop a system to generate test data automatically. The existing such systems do not guarantee to generate test data in only feasible paths. This paper proposes a method to generate feasible test data, using Genetic Algorithm.

An Approach towards Software Quality Assessment

Software engineer needs to determine the real purpose of the software, which is a prime point to keep in mind: The customer’s needs come first, and they include particular levels of quality, not just functionality. Thus, the software engineer has a responsibility to elicit quality requirements that may not even be explicit at the outset and to discuss their importance and the difficulty of attaining them. All processes associated with software quality (e.g. building, checking, improving quality) will be designed with these in mind and carry costs based on the design. Therefore, it is important to have in mind some of the possible attributes of quality. We start by identifying the metrics and measurement approaches that can be used to assess the quality of software product. Most of them can be measured subjectively because there is no solid statistics regarding them. Here, in this paper we propose an approach to measure the software quality statistically.

Optimal Test Sequence Generation in State Based Testing Using Cuckoo Search

The present work describes a method for increasing software testing efficiency by identifying the optimal test sequences in the state machine diagram. The method employs a Meta-heuristic algorithm called Cuckoo Search to investigate best paths in the diagram. It tries to provide a technique for exhaustive coverage with minimal repetition which ensures all transitions coverage and all paths coverage at least once with minimal number of repetitions of states as well as transitions. The algorithm works by maximising an objective function which focuses on most error prone parts of the program so that critical portions can be tested first. State machine diagram is given as input, and Cuckoo Search is performed to generate a list of test sequences as output.