Priti Bansal

Prioritization of test scenarios derived from UML activity diagram using path complexity

This paper presents a novel approach for prioritizing test scenarios generated from UML 2.0 activity graph using path complexity. Activity Diagram is used as it is available at an early stage of the software development life cycle allowing us to detect faults at early stages, hence reducing the overall time and effort required for testing. In the proposed approach, activity diagram is converted into control flow graph and then test scenarios are derived from it using basis path method. The methodology adopted for prioritizing test scenarios is based on path complexity using the concept of path length, information flow metric, predicate node and multiple condition coverage.

A model based approach to test case generation for testing the navigation behavior of dynamic web applications

In todays world web applications have become increasingly popular which mandates high reliability and high quality of web applications. With the growing complexity of web applications, developing a reliable web application in turn requires rigorous testing techniques which are exhaustive, efficient and cost effective. Several researchers have proposed varied methodologies, frameworks and tools to generate test cases for testing web applications by extracting the navigation behavior of web applications using forward or reverse engineering tools. In this paper we propose a Model Based Test Case Generation technique wherein the model representing the navigation behavior of a web application is derived from requirements and low level design. The model is traversed to generate test sequences which can later be incorporated with input data to generate test cases. The proposed approach is also demonstrated by means of a case study.

Integrating greedy based approach with genetic algorithm to generate mixed covering arrays for pair-wise testing

The effectiveness of combinatorial interaction testing (CIT) to test highly configurable systems has constantly motivated researchers to look out for new techniques to construct optimal covering arrays that correspond to test sets. Pair-wise testing is a combinatorial testing technique that generates a pair-wise interaction test set to test all possible combinations of each pair of input parameter value. Meta heuristic techniques have being explored by researchers in past to construct optimal covering arrays for t-way testing (where, t denotes the strength of interaction). In this paper we apply genetic algorithm, a meta heuristic search based optimization algorithm to generate optimal mixed covering arrays for pair-wise testing. Here, we present a novel method that uses a greedy based approach to perform mutation and study the impact of the proposed approach on the performance of genetic algorithm. We describe the implementation of the proposed approach by extending an open source tool PWiseGen. Experimental results indicate that the use of greedy approach to perform mutation improves the performance of genetic algorithm by generating mixed covering arrays with higher fitness level in less number of generations as compared to those generated using other techniques.

Construction of Variable Strength Covering Array for Combinatorial Testing Using a Greedy Approach to Genetic Algorithm

The limitation of time and budget usually prohibits exhaustive testing of interactions between components in a component based software system. Combinatorial testing is a software testing technique that can be used to detect faults in a component based software system caused by the interactions of components in an eective and ecient way. Most of the research in the field of combinatorial testing till now has focused on the construction of optimal covering array (CA) of fixed strength t which covers all t-way interactions among components. The size of CA increases with the increase in strength of testing t, which further increases the cost of testing. However, not all components require higher strength interaction testing. Hence, in a system with k components a technique is required to construct CA of fixed strength t which covers all t-way interactions among k components and all ti-way (where ti > t) interactions between a subset of k components. This is achieved using the variable strength covering array (VSCA). In this paper we propose a greedy based genetic algorithm (GA) to generate optimal VSCA. Experiments are conducted on several benchmark configurations to evaluate the eectiveness of the proposed approach.

ABC-CAG: Covering Array Generator for Pair-wise Testing Using Artificial Bee Colony Algorithm

Testing is an indispensable part of the software development life cycle. It is performed to improve the performance, quality and reliability of the software. Various types of testing such as functional testing and structural testing are performed on software to uncover the faults caused by an incorrect code, interaction of input parameters, etc. One of the major factors in deciding the quality of testing is the design of relevant test cases which is crucial for the success of testing. In this paper we concentrate on generating test cases to uncover faults caused by the interaction of input parameters. It is advisable to perform thorough testing but the number of test cases grows exponentially with the increase in the number of input parameters, which makes exhaustive testing of interaction of input parameters imprudent. An alternative to exhaustive testing is combinatorial interaction testing (CIT) which requires that every t-way interaction of input parameters be covered by at least one test case. Here, we present a novel strategy ABC-CAG (Artificial Bee Colony-Covering Array Generator) based on the Artificial Bee Colony (ABC) algorithm to generate covering an array and a mixed covering array for pair-wise testing. The proposed ABC-CAG strategy is implemented in a tool and experiments are conducted on various benchmark problems to evaluate the ecacy of the proposed approach. Experimental results show that ABC-CAG generates better/comparable results as compared to the existing state-of-the-art algorithms.