Relating Code Coverage, Mutation Score and Test Suite Reducibility to Defect Density

The purpose of the method is to increase the sensitivity of an automatically generated test suite to mutations of a model. Unlike existing methods for generating test scenarios that use the mutational approach to assess the resulting test set, the proposed method analyzes the possibility of detecting mutations on the fly, in the process of analyzing the model’s behavior space, by adding of special coverage goals. Two types of mutants manifestation are considered: deviations in the behavior of paths for (weak case) and in the observed output (strong case). A new algorithm is proposed for efficient search of a path with observable effect of a mutation.

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K-Step Crossover Method based on Genetic Algorithm for Test Suite Prioritization in Regression Testing

JUCS - Journal of Universal Computer Science ◽

10.3897/jucs.65241 ◽

2021 ◽

Vol 27 (2) ◽

pp. 170-189

Author(s):

P. K. Gupta

Keyword(s):

Genetic Algorithm ◽

Regression Testing ◽

Test Suite ◽

Test Case ◽

Test Cases ◽

Test Case Prioritization ◽

Average Percentage ◽

Code Coverage ◽

Test Case Selection ◽

Case Selection

Software is an integration of numerous programming modules  (e.g., functions, procedures, legacy system, reusable components, etc.) tested and combined to build the entire module. However, some undesired faults may occur due to a change in modules while performing validation and verification. Retesting of entire software is a costly affair in terms of money and time. Therefore, to avoid retesting of entire software, regression testing is performed. In regression testing, an earlier created test suite is used to retest the software system's modified module. Regression Testing works in three manners; minimizing test cases, selecting test cases, and prioritizing test cases. In this paper, a two-phase algorithm has been proposed that considers test case selection and test case prioritization technique for performing regression testing on several modules ranging from a smaller line of codes to huge line codes of procedural language. A textual based differencing algorithm has been implemented for test case selection. Program statements modified between two modules are used for textual differencing and utilized to identify test cases that affect modified program statements. In the next step, test case prioritization is implemented by applying the Genetic Algorithm for code/condition coverage. Genetic operators: Crossover and Mutation have been applied over the initial population (i.e. test cases), taking code/condition coverage as fitness criterion to provide a prioritized test suite. Prioritization algorithm can be applied over both original and reduced test suite depending upon the test suite's size or the need for accuracy. In the obtained results, the efficiency of the prioritization algorithms has been analyzed by the Average Percentage of Code Coverage (APCC) and Average Percentage of Code Coverage with cost (APCCc). A comparison of the proposed approach is also done with the previously proposed methods and it is observed that APCC & APCCc values achieve higher percentage values faster in the case of the prioritized test suite in contrast to the non-prioritized test suite.

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Generating Effective Test Suite for Multiparameter Software using ACTS Tool and its Verification using Code Coverage Tools

International Journal of Scientific and Engineering Research ◽

10.14299/ijser.2018.08.04 ◽

2018 ◽

Vol 9 (8) ◽

pp. 1579-1582

Author(s):

Abhinandan H. Patil ◽

Neena Goveas ◽

Krishnan Rangarajan

Keyword(s):

Empirical Data ◽

Practical Method ◽

Test Suite ◽

Combinatorial Testing ◽

Code Coverage ◽

Line Method ◽

System Under Test ◽

Time Table ◽

Multiple Input ◽

Input Parameters

Combinatorial testing is a practical method to test software with multiple input parameters. National Institute of Standards and Technology has developed tools which aid combinatorial testing. ACTS is one such tool which is freely available to users. In spite of this, very few software being developed are being tested systematically. In this paper we explore the effectiveness and suitability of ACTS tool to test software which has a m ultiparameter input. We chose a Java based software, College Time Table, a software which involves multiparameter input, as system under test. We could achieve 90% coverage of instructions, line, method and 100% class coverage with practical time and effort with ACTS tool. The process involved in getting above mentioned results is documented in this paper. Empirical data generated with the code coverage confirms the effectiveness of ACTS generated test suite for a simple project.

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Functional Test Case Generator for Contiki Operating System and Cooja Simulator

International Journal of Scientific and Engineering Research ◽

10.14299/ijser.2019.01.07 ◽

2019 ◽

Vol 10 (1) ◽

pp. 1251-1257

Author(s):

Abhinandan H Patil

Keyword(s):

Operating System ◽

Functional Test ◽

Test Suite ◽

Target System ◽

Test Case ◽

Test Cases ◽

Code Coverage ◽

Regression Test ◽

Run Time ◽

Configuration Systems

Evolving multi-parameter, multi-configuration systems require regression test suite that can be customized. This is in terms of run time. Run time can be customized by generating the combinations using combinatorial techniques. For systems like Contiki operating system, the test cases need to be executed in its simulator Cooja. Executing test cases in a simulator requires functional test cases to be generated from the combinatorial parameter combinations obtained. In this work we present a methodology to generate the functional test cases. We present Functional Test Case Generator for Contiki and Cooja (FTCGCC), which is a tool developed using our methodology. We demonstrate use of our tool by generating customizable regression test suite for Contiki and Cooja using code coverage as criteria. FTCGCC is developed for the test case generation when target System Under Test is IoT operating system Contiki and its simulator Cooja.

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