Open AccessSIMILARITY DISTANCE MEASURE AND PRIORITIZATION ALGORITHM FOR TEST CASE PRIORITIZATION IN SOFTWARE PRODUCT LINE TESTING
Author(s) -
Shahliza Abd Halim,
Dayang Norhayati Abang Jawawi,
Muhammad Sahak
Publication year - 2018
Publication title -
journal of ict
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.217
H-Index - 10
eISSN - 2180-3862
pISSN - 1675-414X
DOI - 10.32890/jict2019.18.1.8281
Subject(s) - prioritization , computer science , requirement prioritization , measure (data warehouse) , fault detection and isolation , data mining , fault (geology) , similarity (geometry) , reliability engineering , product (mathematics) , test case , software , algorithm , machine learning , engineering , mathematics , artificial intelligence , software system , software construction , geometry , regression analysis , management science , seismology , actuator , image (mathematics) , programming language , geology
To achieve the goal of creating products for a specific market segment, implementation of Software Product Line (SPL) is required to fulfill specific needs of customers by managing a set of common features and exploiting the variabilities between the products. Testing product-by-product is not feasible in SPL due to the combinatorial explosion of product number, thus, Test Case Prioritization (TCP) is needed to select a few test cases which could yield high number of faults. Among the most promising TCP techniques is similarity-based TCP technique which consists of similarity distance measure and prioritization algorithm. The goal of this paper is to propose an enhanced string distance and prioritization algorithm which could reorder the test cases resulting to higher rate of fault detection. Comparative study has been done between different string distance measures and prioritization algorithms to select the best techniques for similarity-based test case prioritization. Identified enhancements have been implemented to both techniques for a better adoption of prioritizing SPL test cases. Experiment has been done in order to identify the effectiveness of enhancements done for combination of both techniques. Result shows the effectiveness of the combination where it achieved highest average fault detection rate, attained fastest execution time for highest number of test cases and accomplished 41.25% average rate of fault detection. The result proves that the combination of both techniques improve SPL testing effectiveness compared to other existing techniques.