Evolved regression test suite selection using BCO and GA and empirical comparison with ACO

Suri, Bharti; Singhal, Shweta

doi:10.1007/s40012-016-0080-5

Cited by 7 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the newly developed tool to provide such good results is rather impressive. [9] in terms of different languages:…”

Section: Resultsmentioning

confidence: 99%

Software Test Suite Minimization using Ant Colony Optimization

2019

IJITEE

View full text Add to dashboard Cite

In the fast pacing technological era, the key to a successful software industry is quick delivery of high quality software to the clients. This high quality is achieved by performing software testing on the product. The high quality product ensures stakeholder’s satisfaction which in turn spreads good word about the software industry making it a success. In this paper, we will focus on the problems faced during regression testing and how the same can be handled. Regression testing is a critical activity done during the software maintenance phase of the software development cycle. However, it has countless underlying issues like effective test case generation and prioritization, etc which need to be dealt with. These issues demand effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of Ant Colony Optimization (ACO) for test suite minimization has been an area of interest for many researchers. This paper presents an implementation of ACO for test suite minimization, showcasing how arbitrary nature of ACO helps choose an optimal solution to the problem.

show abstract

“…For the newly developed tool to provide such good results is rather impressive. [9] in terms of different languages:…”

Section: Resultsmentioning

confidence: 99%

Software Test Suite Minimization using Ant Colony Optimization

2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…In contrast, Gaur and Arif [102] performed TSR by using the ant colony algorithm. The authors devloped a tool, ANT-K, and evaluated their proposed technique performance by comparing it with MHBG-TCS [103] based on attained fault coverage and test ET. They reported that the proposed approach attained better performance.…”

Section: Swarm Intelligence-basedmentioning

confidence: 99%

“…The authors also developed an MHBG-TCS tool. They compared the performance of MHBG-TCS with ACO [103] and applied it to 17 open-source programmes. Finally, they concluded that MHBG-TCS attained near-optimal performance compared to ACO.…”

Section: Studymentioning

confidence: 99%

A systematic review on search‐based test suite reduction: State‐of‐the‐art, taxonomy, and future directions

2023

View full text Add to dashboard Cite

Regression testing remains a promising research area for the last few decades. It is a type of testing that aims at ensuring that recent modifications have not adversely affected the software product. After the introduction of a new change in the system under test, the number of test cases significantly increases to handle the modification. Consequently, it becomes prohibitively expensive to execute all of the generated test cases within the allocated testing time and budget. To address this situation, the test suite reduction (TSR) technique is widely used that focusses on finding a representative test suite without compromising its effectiveness such as fault‐detection capability. In this work, a systematic review study is conducted that intends to provide an unbiased viewpoint about TSR based on various types of search algorithms. The study's main objective is to examine and classify the current state‐of‐the‐art approaches used in search‐based TSR contexts. To achieve this, a systematic review protocol is adopted and, the most relevant primary studies (57 out of 210) published between 2007 and 2022 are selected. Existing search‐based TSR approaches are classified into five main categories, including evolutionary‐based, swarm intelligence‐based, human‐based, physics‐based, and hybrid, grounded on the type of employed search algorithm. Moreover, the current work reports the parameter settings according to their category, the type of considered operator(s), and the probabilistic rate that significantly impacts on the quality of the obtained solution. Furthermore, this study describes the comparison baseline techniques that support the empirical comparison regarding the cost‐effectiveness of a search‐based TSR approach. Finally, it isconcluded that search‐based TSR has great potential to optimally solve the TSR problem. In this regard, several potential research directions are outlined as useful for future researchers interested in conducting research in the TSR domain.

show abstract

“…Test case selection prioritization performed within a time-constrained environment happens to be a combinatorial optimization problem. Evolutionary techniques like Ant Colony Optimization (ACO) (1) , Bee Colony Optimization (BCO) (2) , and Genetic Algorithms (GA) (3) , etc. are approaches built around the physical behavior of ants, https://www.indjst.org/ bees, and human DNA and based on these the test case selection & prioritization has tried to be solved.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, Bee Colony Optimization (BCO) was established for TCSP in (2) . A hybrid approach combining Genetic Algorithms (GA) and BCO was also implemented for TCSP (3) . Recently, in 2019 a greedy approach based on Set Cover in a time-constrained environment was proposed (4) .…”

Section: Introductionmentioning

confidence: 99%

Empirical Evaluation of Tetrad Optimization Methods for Test Case Selection and Prioritization

Singhal¹,

Jatana²,

Dhand³

et al. 2023

IJST

View full text Add to dashboard Cite

Objectives: Software researchers have been taking advantage of various evolutionary optimization approaches by digitizing them. Test case selection and prioritization based on fault coverage criteria within a time-constrained environment is important in regression testing problem. Methods: This work empirically evaluates different approaches that includes evolutionary approaches (Ant Colony Optimization, Bee Colony Optimization, a combination of Genetic Algorithms and Bee Colony optimization), and a Greedy approach. These tetrad techniques have been successfully applied to regression testing. Also, tools have been developed for their implementation. Eight open-source test programs, written in C language have been used for empirical evaluation of the regression testing approaches. Findings: The accuracy achieved by t-GSC, being a greedy technique, was found to be least; while that of ACO was found to be the best. All the tetrad approaches yielded borderline better or worse results, while all the four gave excellent time and size gains. Novelty: There are many studies available in the literature that compare various regression testing approaches of a similar kind. Instead of repeating the same, it is intended to evaluate two well-accepted approximation approaches: a hybrid approach, and a greedy approach. It has been tried to evaluate the efficiency of the greedy approach with the metaheuristic approach. It is imperative to compare approaches following different algorithmic paradigms, yet trying to solve the same problem.

show abstract

Evolved regression test suite selection using BCO and GA and empirical comparison with ACO

Cited by 7 publications

References 34 publications

Software Test Suite Minimization using Ant Colony Optimization

Software Test Suite Minimization using Ant Colony Optimization

A systematic review on search‐based test suite reduction: State‐of‐the‐art, taxonomy, and future directions

Empirical Evaluation of Tetrad Optimization Methods for Test Case Selection and Prioritization

Contact Info

Product

Resources

About