scholarly journals Assessing the Test Suite of a Large System Based on Code Coverage, Efficiency and Uniqueness

2016 ◽  
Author(s):  
Laszlo Vidacs ◽  
Ferenc Horvath ◽  
David Tengeri ◽  
Arpad Beszedes
Keyword(s):  
Large System ◽  
Test Suite ◽  
Code Coverage

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

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.

Functional Test Case Generator for Contiki Operating System and Cooja Simulator

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.

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

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.

Statement frequency coverage: A code coverage criterion for assessing test suite effectiveness

2021 ◽  
Vol 129 ◽  
pp. 106426
Author(s):  
Alireza Aghamohammadi ◽  
Seyed-Hassan Mirian-Hosseinabadi ◽  
Sajad Jalali
Keyword(s):  
Test Suite ◽  
Code Coverage ◽  
Coverage Criterion ◽  
Frequency Coverage

scholarly journals REDUNET: reducing test suites by integrating set cover and network-based optimization

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Misael Mongiovì ◽  
Andrea Fornaia ◽  
Emiliano Tramontana
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Automatic Generation ◽  
Control Flow ◽  
Test Suite ◽  
Set Cover ◽  
Software Systems ◽  
Code Coverage ◽  
Test Suite Reduction ◽  
Test Suites

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.

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