Prioritizing test cases for regression techniques using metaheuristic techniques

Mittal, Shweta; Sangwan, Om Prakash

doi:10.1080/02522667.2017.1372150

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…The method of selecting test cases is to arrange them in a logical sequence based on several factors that improves the test cases' efficacy in reaching a performance target. Test case prioritization (TCP) is the process of scheduling test cases in this manner [9]. Running all test cases in an existing test suite can take an excessive amount of time.…”

Section: Test Case Prioritizationmentioning

confidence: 99%

Test Case Prioritization Using Swarm Intelligence Algorithm to Improve Fault Detection and Time for Web Application

Mohan,

Zainal

2023

JSCDM

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Prioritizing test cases based on several parameters where important ones are executed first is known as test case prioritization (TCP). Code coverage, functionality, and features are all possible factors of TCP for detecting bugs in software as early as possible. This research was carried out to test and compare the effectiveness Swarm Intelligence algorithms, where Artificial Bee Colony (ABC) and Ant Colony Optimization (ACO) algorithms were implemented to find the fault detected and execution time as these are the curial aspects in software testing to ensure good quality products are produced within the timeline. As web applications are commonly used by a board population, this research was carried out on an Online Shopping application represented as Case Study One and Education Administrative application known as Case Study Two. In recent years, TCP has been implemented widely, but none has implemented on web application which was conducted to fill the gaps and produce a new contribution in this area. The outcome was compared using Average Percentage Fault Detected (APFD) and execution time. For Case Study One, the APFD value was 0.80 and 0.71 while the execution time was 8.64 seconds and 0.69 seconds respectively for ABC and ACO. For Case Study Two, the APFD values were 0.81 and 0.64 while the execution time was 8.83 seconds and 1.22 seconds for ABC and ACO. It was seen that both algorithms performed well in their respective ways. ABC had shown to give a higher value for APFD while ACO had converged faster for execution time.

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Section: Test Case Prioritizationmentioning

confidence: 99%

Test Case Prioritization Using Swarm Intelligence Algorithm to Improve Fault Detection and Time for Web Application

Mohan,

Zainal

2023

JSCDM

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“…In the study that was conducted by Mittal and Sangwan [10], they have surfed literature and came out with the examined meta-heuristic algorithms and how they performed in the domain of test selection, minimization, and test prioritization. They also addressed the problem of choosing a proper fitness metric for such an optimization problem and concluded that the Average Percentage of Fault Detected (APFD) is the most frequently and widely used metric nowadays.…”

Section: Single Objective Studiesmentioning

confidence: 99%

Comparison of Multi-Objective Evolutionary Algorithms to Prioritize Regression Test Cases

2021

IJETER

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Regression testing is one of the most critical testing activities among software product verification activities. Nevertheless, resources and time constraints could inhibit the execution of a full regression test suite, hence leaving us in confusion on what test cases to run to preserve the high quality of software products. Different techniques can be applied to prioritize test cases in resource-constrained environments, such as manual selection, automated selection, or hybrid approaches. Different Multi-Objective Evolutionary Algorithms (MOEAs) have been used in this domain to find an optimal solution to minimize the cost of executing a regression test suite while obtaining maximum fault detection coverage as if the entire test suite was executed. MOEAs achieve this by selecting set of test cases and determining the order of their execution. In this paper, three Multi Objective Evolutionary Algorithms, namely, NSGA-II, IBEA and MoCell are used to solve test case prioritization problems using the fault detection rate and branch coverage of each test case. The paper intends to find out what’s the most effective algorithm to be used in test cases prioritization problems, and which algorithm is the most efficient one, and finally we examined if changing the fitness function would impose a change in results. Our experiment revealed that NSGA-II is the most effective and efficient MOEA; moreover, we found that changing the fitness function caused a significant reduction in evolution time, although it did not affect the coverage metric.

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A review on nature inspired algorithm for test suite optimization

Ahuja¹,

Bhatia²,

Rani³

2023

RECENT ADVANCES IN SCIENCES, ENGINEERING, INFORMATION TECHNOLOGY &Amp; MANAGEMENT

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Prioritizing test cases for regression techniques using metaheuristic techniques

Cited by 5 publications

References 6 publications

Test Case Prioritization Using Swarm Intelligence Algorithm to Improve Fault Detection and Time for Web Application

Test Case Prioritization Using Swarm Intelligence Algorithm to Improve Fault Detection and Time for Web Application

Comparison of Multi-Objective Evolutionary Algorithms to Prioritize Regression Test Cases

A review on nature inspired algorithm for test suite optimization

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