ABC-CAG: Covering Array Generator for Pair-wise Testing Using Artificial Bee Colony Algorithm
Journal Title: e-Informatica Software Engineering Journal - Year 2016, Vol 10, Issue 1
Abstract
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 efficacy of the proposed approach. Experimental results show that ABC-CAG generates better/comparable results as compared to the existing state-of-the-art algorithms.
Authors and Affiliations
Priti Bansal, Sangeeta Sabharwal, Nitish Mittal, Sarthak Arora
Keywords
Related Articles
- EP ID EP201183
- DOI 10.5277/e-Inf160101
- Views 102
- Downloads 0
Data Flow Approach to Testing Java Programs Supported with DFC
Code based (``white box'') approach to testing can be divided into two main types: control flow coverage and data flow coverage. The data flow testing was introduced to structural programming languages and later adopted...
Model Driven Web Engineering: A Systematic Mapping Study
Background: Model Driven Web Engineering (MDWE) is the application of the model driven paradigm to the domain of Web software development, where it is particularly helpful because of the continuous evolution of Web techn...
Knowledge Management in Software Testing: A Systematic Snowball Literature Review
Description : Software testing benefits from the usage of Knowledge Management (KM) methods and principles. Thus, there is a need to adopt KM to the software testing core processes and attain the benefits that it provide...
Experience Report: Towards Extending an OSEK-Compliant RTOS with Mixed Criticality Support
Background: With an increase of the number of features in a vehicle, the computational requirements also increase, and vehicles may contain up to 100 Electronic Control Units (ECUs) to accommodate these requirements. For...
NRFixer: Sentiment Based Model for Predicting the Fixability of Non-Reproducible Bugs
Software maintenance is an essential step in software development life cycle. Nowadays, software companies spend approximately 45% of total cost in maintenance activities. Large software projects maintain bug repositorie...