scholarly journals Making the Case for MORTO: Multi Objective Regression Test Optimization

2011 ◽  
Author(s):  
Mark Harman
Keyword(s):  
Multi Objective ◽  
Regression Test ◽  
Test Optimization

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

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1454
Keyword(s):  
Fault Detection ◽  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Optimal Solution ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Nsga Ii ◽  
Multi Objective ◽  
Regression Test

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.

Regression test optimization and prioritization using Honey Bee optimization algorithm with fuzzy rule base

2020 ◽  
Author(s):  
Soumen Nayak ◽  
Chiranjeev Kumar ◽  
Sachin Tripathi ◽  
Nirjharini Mohanty ◽  
Vishal Baral
Keyword(s):  
Honey Bee ◽  
Optimization Algorithm ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Fuzzy Rule Base ◽  
Regression Test ◽  
Test Optimization

scholarly journals Multi-objective regression test suite optimization using three variants of adaptive neuro fuzzy inference system

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242708
Author(s):  
Ayesha Kiran ◽  
Wasi Haider Butt ◽  
Arslan Shaukat ◽  
Muhammad Umar Farooq ◽  
Urooj Fatima ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Current System ◽  
Test Suite ◽  
Test Cases ◽  
Inference System ◽  
Multi Objective ◽  
Neuro Fuzzy ◽  
Regression Test ◽  
Test Suites

In the process of software development, regression testing is one of the major activities that is done after making modifications in the current system or whenever a software system evolves. But, the test suite size increases with the addition of new test cases and it becomes in-efficient because of the occurrence of redundant, broken, and obsolete test cases. For that reason, it results in additional time and budget to run all these test cases. Many researchers have proposed computational intelligence and conventional approaches for dealing with this problem and they have achieved an optimized test suite by selecting, minimizing or reducing, and prioritizing test cases. Currently, most of these optimization approaches are single objective and static in nature. But, it is mandatory to use multi-objective dynamic approaches for optimization due to the advancements in information technology and associated market challenges. Therefore, we have proposed three variants of self-tunable Adaptive Neuro-fuzzy Inference System i.e. TLBO-ANFIS, FA-ANFIS, and HS-ANFIS, for multi-objective regression test suites optimization. Two benchmark test suites are used for evaluating the proposed ANFIS variants. The performance of proposed ANFIS variants is measured using Standard Deviation and Root Mean Square Error. A comparison of experimental results is also done with six existing methods i.e. GA-ANFIS, PSO-ANFIS, MOGA, NSGA-II, MOPSO, and TOPSIS and it is concluded that the proposed method effectively reduces the size of regression test suite without a reduction in the fault detection rate.

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