A code coverage-based test suite reduction and prioritization framework

Author(s):  
Saif Ur Rehman Khan ◽  
Sai Peck Lee ◽  
Reza Meimandi Parizi ◽  
Manzoor Elahi
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Misael Mongiovì ◽  
Andrea Fornaia ◽  
Emiliano Tramontana

Abstract The availability of effective test suites is critical for the development and maintenance of reliable software systems. To increase test effectiveness, software developers tend to employ larger and larger test suites. The recent availability of software tools for automatic test generation makes building large test suites affordable, therefore contributing to accelerating this trend. However, large test suites, though more effective, are resources and time consuming and therefore cannot be executed frequently. Reducing them without decreasing code coverage is a needed compromise between efficiency and effectiveness of the test, hence enabling a more regular check of the software under development. We propose a novel approach, namely REDUNET, to reduce a test suite while keeping the same code coverage. We integrate this approach in a complete framework for the automatic generation of efficient and effective test suites, which includes test suite generation, code coverage analysis, and test suite reduction. Our approach formulates the test suite reduction as a set cover problem and applies integer linear programming and a network-based optimisation, which takes advantage of the properties of the control flow graph. We find the optimal set of test cases that keeps the same code coverage in fractions of seconds on real software projects and test suites generated automatically by Randoop. The results on ten real software systems show that the proposed approach finds the optimal minimisation and achieves up to 90% reduction and more than 50% reduction on all systems under analysis. On the largest project our reduction algorithm performs more than three times faster than both integer linear programming alone and the state-of-the-art heuristic Harrold Gupta Soffa.


Author(s):  
Samaila Musa Et.al

Most of the test cases minimization reduced test cases during regression testing   to generate new test suite to cover the same software requirements.The objective of this paper is to present new framework that integrate the idea of minimization and prioritization.Hence, reduction and prioritization able to reduce test cases based on the statements covered by the previous test cases to avoid redundancy.Beginning from the reduction of the test cases, followed by  weighted prioritizationaccording to their usefulness.The  framework was tested using sample test suite and the results obtained shown increases on the average percentage  of faults detection (APFD). Future plan is to test on the larger size of test suite.


2016 ◽  
Vol 36 (6) ◽  
pp. 963-975 ◽  
Author(s):  
Saif Ur Rehman Khan ◽  
Sai Peck Lee ◽  
Raja Wasim Ahmad ◽  
Adnan Akhunzada ◽  
Victor Chang

1998 ◽  
Vol 40 (5-6) ◽  
pp. 347-354 ◽  
Author(s):  
T.Y. Chen ◽  
M.F. Lau

Author(s):  
B. Subashini ◽  
D. Jeya Mala

Software testing is used to find bugs in the software to provide a quality product to the end users. Test suites are used to detect failures in software but it may be redundant and it takes a lot of time for the execution of software. In this article, an enormous number of test cases are created using combinatorial test design algorithms. Attribute reduction is an important preprocessing task in data mining. Attributes are selected by removing all weak and irrelevant attributes to reduce complexity in data mining. After preprocessing, it is not necessary to test the software with every combination of test cases, since the test cases are large and redundant, the healthier test cases are identified using a data mining techniques algorithm. This is healthier and the final test suite will identify the defects in the software, it will provide better coverage analysis and reduces execution time on the software.


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