IRPS – An Efficient Test Data Generation Strategy For Pairwise Testing.

Software testing is an integral part of software engineering. Lack of testing often leads to disastrous consequences including loss of data, fortunes, and even lives

An Enhanced Pairwise Search Approach for Generating Optimum Number of Test Data and Reduce Execution Time

In recent days testing considers the most important task for building software that is free from error. Since the resources and time is limited to produce software, hence, it is not possible of performing exhaustive tests (i.e. to test all possible combinations of input data.) An alternative to get ride from this type exhaustive numbers and as well to reduce cost, an approach called Pairwise (2 way) test data generation approach will be effective. Most of the software faults in pairwise approach caused by unusual combination of input data.  Hence, the demand for the optimization of number of generated test-cases and reducing the execution time is growing in software industries. This paper proposes an enhancement in pairwise search approach which generates optimum number of input values for testing purposes.  In this approach it searches the most coverable pairs by pairing parameters and adopts one-test-at-a-time strategy for constructing a final test-suite.  Compared to other existing strategies, Our proposed approach is effective in terms of number of generated test cases and of execution time. Keywords:, Software testing, Pairwise testing, Combinatorial interaction testing, Test case generation

Artificial Bee Colony Algorithm for Pairwise Test Generation

Our dependence on software applications has become dramatic in many activities of our daily life as they help to increase the efficiency of our tasks. These software applications have many sets of input values, parameters, software/hardware environments and system conditions, which need to be tested to ensure software reliability and quality. However, the whole comprehensive software testing is virtually not possible due to marketing pressure and resource constraints. In an attempt to solve this problem, there has been a development of a number of sampling and pairwise strategies in the literature. In this paper, we evaluated and proposed a pairwise strategy named Pairwise Artificial Bee Colony algorithm (PABC). According to the benchmarking results, the PABC strategies outdo some existing strategies to generate a test case in many of the system configurations taken into consideration. In a case where PABC is not at its optimal stage or its best performance, the experiments of a test case are effectively competitive. PABC progresses as a means to achieve the effective use of the artificial bee colony algorithm for pairwise testing reduction

The Design and Implementation of a Pairwise Strategy Supporting Constraints and Seeding Mechanism

This paper describes the design and development of a pairwise test data generation, called 2TG, supporting seeding and constraints. In doing so, a number of experiments are discussed in order to prove the correctness of the implementation

HABCSm: A Hamming Based t-way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation

Search-based software engineering that involves the deployment of meta-heuristics in applicable software processes has been gaining wide attention. Recently, researchers have been advocating the adoption of meta-heuristic algorithms for t-way testing strategies (where t points the interaction strength among parameters). Although helpful, no single meta-heuristic based t-way strategy can claim dominance over its counterparts. For this reason, the hybridization of meta-heuristic algorithms can help to ascertain the search capabilities of each by compensating for the limitations of one algorithm with the strength of others. Consequently, a new meta-heuristic based t-way strategy called Hybrid Artificial Bee Colony (HABCSm) strategy, based on merging the advantages of the Artificial Bee Colony (ABC) algorithm with the advantages of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. HABCSm is the first t-way strategy to adopt Hybrid Artificial Bee Colony (HABC) algorithm with Hamming distance as its core method for generating a final test set and the first to adopt the Hamming distance as the final selection criterion for enhancing the exploration of new solutions. The experimental results demonstrate that HABCSm provides superior competitive performance over its counterparts. Therefore, this finding contributes to the field of software testing by minimizing the number of test cases required for test execution

An orchestrated survey of available algorithms and tools for Combinatorial Testing

For functional testing based on the input domain of a functionality, parameters and their values are identified and a test suite is generated using a criterion exercising combinations of those parameters and values. Since software systems are large, resulting in large numbers of parameters and values, a technique based on combinatorics called Combinatorial Testing (CT) is used to automate the process of creating those combinations. CT is typically performed with the help of combinatorial objects called Covering Arrays. The goal of the present work is to determine available algorithms/tools for generating a combinatorial test suite. We tried to be as complete as possible by using a precise protocol for selecting papers describing those algorithms/tools. The 75 algorithms/tools we identified are then categorized on the basis of different comparison criteria, including: the test suite generation technique, the support for selection (combination) criteria, mixed covering array, the strength of coverage, and the support for constraints between parameters. Results can be of interest to researchers or software companies who are looking for a CT algorithm/tool suitable for their needs

The ‘Big Four’ price promotions in predicting decision utility and efficacy

One way that retailers help the consumer make choices is via promotions – price framing methods that explicitly offer a price reduction of value for money off the regular retail price (RRP). However, there is a growing body of research that has indicated that merely the word ‘promotion’ or ‘deal’ can increase purchase intentions despite the deal offering no savings. Despite these findings, almost no research has quantifiably considered which, how and to what extent different promotional methods can bias decisions. Furthermore, very little is known about how consumers go about making promotional decisions or which psychological factors impact the decision-making process. Considering a broad range of decision-making frameworks and psychological theories, this thesis aims to explore the extent that promotional practices influence decision-making outcomes. Furthermore, it will consider how psychological traits like financial literacy, experience and brand relationships moderate any found effects. To achieve these objectives the effect of the four most common promotional practices on decision utility will be tested in light of: the previous literature on decision-making and promotions (Chapter 1); expert interviews describing the traits or behaviours important in developing promotional strategies (Chapter 2); the effect of information processing on promotional decision making (Chapter 3); how prices are internalised (Chapter 4); and consumer relationships (Chapter 5). Finally, the results of each chapter will be used to create and test a framework of promotional decision-making. Creating and testing this framework in an experimental and more ecologically valid setting, i.e. a virtual supermarket will be the sole purpose of Chapter 6. The aim of creating and validating the framework will be to significantly contribute to: academia, by adding some novel research to the growing promotional literature; and practice, by considering how the practices specific effects to decision making can impact fair pricing practices and consumer education