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

Development of an automated test data generation and execution strategy using combinatorial approach [TK1-9971].

Untuk memastikan tahap piawaian jaminan kualiti dan keboleharapan sesuatu perisian, pengujian hendaklah dijalankan untuk setiap konfigurasi. To ensure acceptable level of quality and reliability of a typical software product, it is desirable to test every possible combination of input data under various configurations

Development Of An Automated Test Data Generation And Execution Strategy Using Combinatorial Approach

Untuk Memastikan Tahap Piawaian Jaminan Kualiti Dan Keboleharapan Sesuatu Perisian, Pengujian Hendaklah Dijalankan Untuk Setiap Konfigurasi. To Ensure Acceptable Level Of Quality And Reliability Of A Typical Software Product, It Is Desirable To Test Every Possible Combination Of Input Data Under Various Configurations

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

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 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

Optimal Minimisation of Pairwise-covering Test Configurations Using Constraint Programming

International audienceContext: Testing highly-configurable software systems is challenging due to a large number of test configurations that have to be carefully selected in order to reduce the testing effort as much as possible, while maintaining high software quality. Finding the smallest set of valid test configurations that ensure sufficient coverage of the system's feature interactions is thus the objective of validation engineers, especially when the execution of test configurations is costly or time-consuming. However, this problem is NP-hard in general and approximation algorithms have often been used to address it in practice. Objective: In this paper, we explore an alternative approach based on constraint programming that will allow engineers to increase the effectiveness of configuration testing while keeping the number of configurations as low as possible. Method: Our approach consists in using a (time-aware) minimisation algorithm based on constraint programming. Given the amount of time, our solution generates a minimised set of valid test configurations that ensure coverage of all pairs of feature values (a.k.a. pairwise coverage). The approach has been implemented in a tool called PACOGEN. Results: PACOGEN was evaluated on 224 feature models in comparison with the two existing tools that are based on a greedy algorithm. For 79% of 224 feature models, PACOGEN generated up to 60% fewer test configurations than the competitor tools. We further evaluated PACOGEN in the case study of large industrial highly-configurable video conferencing software with a feature model of 169 features, and found 60% fewer configurations compared with the manual approach followed by test engineers. The set of test configurations generated by PACOGEN decreased the time required by test engineers in manual test configuration by 85%, increasing the feature-pairs coverage at the same time. Conclusion: Extensive evaluation concluded that optimal minimisation of pairwise-covering test configurations is efficiently addressed using constraint programming techniques

A Tabu Search hyper-heuristic strategy for t-way test suite generation

This paper proposes a novel hybrid t-way test generation strategy (where t indicates interaction strength), called High Level Hyper-Heuristic (HHH). HHH adopts Tabu Search as its high level meta-heuristic and leverages on the strength of four low level meta-heuristics, comprising of Teaching Learning based Optimization, Global Neighborhood Algorithm, Particle Swarm Optimization, and Cuckoo Search Algorithm. HHH is able to capitalize on the strengths and limit the deficiencies of each individual algorithm in a collective and synergistic manner. Unlike existing hyper-heuristics, HHH relies on three defined operators, based on improvement, intensification and diversification, to adaptively select the most suitable meta-heuristic at any particular time. Our results are promising as HHH manages to outperform existing t-way strategies on many of the benchmarks