Finding a Test Order using Object-Oriented Slicing Technique

Jaroenpiboonkit, J.; Suwannasart, Taratip

doi:10.1109/apsec.2007.58

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…Differently from the works that consider the type of relationships between the classes, the work of Jaroenpibooki et al [18,19] uses a TDG and class slicing techniques. The stubs are constructed considering isolated parts from the classes.…”

Section: The Class and Integration Test Order Problemmentioning

confidence: 99%

“…After that, for each objective, a local search is performed on each ant (LocalSearch), lines 12 and 13. Finally, all test orders (s 1 ,s 2 ) of the ants are evaluated and a global pheromone update is performed (GlobalPheromoneUpdate) based on the best solutions of the iteration, lines [16][17][18][19]. The archive of the best solutions found so far, the approximation of the Pareto front, is also updated (ParetoSetUpdate), line 20.…”

Section: The Paco Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Multi-objective optimization algorithms applied to the class integration and test order problem

Vergílio

Pozo

Árias

et al. 2012

Int J Softw Tools Technol Transfer

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In the context of object-oriented software, a common problem is the determination of test orders for the integration test of classes, known as the class integration and test order (CITO) problem. The existing approaches, based on graphs, usually generate solutions that are sub-optimal, and do not consider the different factors and measures that can affect the construction of stubs. To overcome this limitation, solutions based on genetic algorithms (GA) have presented promising results. However, the determination of a cost function, which is able to generate the best solution, is not always a trivial task, mainly for complex systems. Therefore, to better represent the CITO problem, we introduce, in this paper, a multi-objective optimization approach, to generate a set of good solutions that achieve a balanced compromise between the different measures (objectives). Three different multi-objective optimization algorithms (MOA) were implemented: Pareto ant colony, multi-objective Tabu search and non-dominated sorting GA. The approach is applied to real programs and the obtained results allow comparison with the simple GA approach and evaluation of the different MOA.

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Section: The Class and Integration Test Order Problemmentioning

confidence: 99%

Section: The Paco Algorithmmentioning

confidence: 99%

Multi-objective optimization algorithms applied to the class integration and test order problem

Vergílio

Pozo

Árias

et al. 2012

Int J Softw Tools Technol Transfer

View full text Add to dashboard Cite

show abstract

“…A scenario coverage criterion based on simple paths has been applied to the SG to find weighted paths for every test scenario. Object oriented slicing based prioritization was solicited by [18]. Class slicing technique was utilized by the authors to isolate the interesting parts from rest of program.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning based Test Case Prioritization in Object Oriented Testing

Singh¹,

Bhatia²,

Singhrova³

2019

IJRTE

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Software maintenance is one of the most expensive activities in software life cycle. It costs nearly 70% of the total cost of the software. Either to adopt the new requirement or to correct the functionality, software undergoes maintenance. As a consequent of maintenance activities, software undergoes many reforms. Newly added software components may affect the working of existing components and also may introduce faults in existing components. The regression testing tries to reveal the faults that might have been introduced due to these reformations. Running all the prior existing test cases may not be feasible due to constraints like time, cost and resources. Test case prioritization may help in ordered execution of test cases. Running a faulty or fault prone component early in testing process may help in revealing more faults per unit of time. And hence may reduce the testing time. There have been many different criteria for assigning the priority to test cases. But none of the approaches so far have considered the object oriented design metrics for determining the priority of test cases. Object oriented design metrics have been empirically studied for their impact of software maintainability, reliability, testability and quality but usage of these metrics in test case prioritization is still an open area of research. The research reported in this paper evaluates subset of CK metrics. Metrics considered from CK suite include Coupling between objects (CBO), Depth of Inheritance tree (DIT), weighted methods per class (WMC), Number of children (NOC), and Response for a class (RFC). Study also considers four other metrics namely publically inherited methods (PIM), weighted attributes per class (WAC), number of methods inherited (NMI) and number of methods overridden. A model is built based on these metrics for the prediction of software quality and based on the quality measures software modules are classified with the help of Support Vector Machine (SVM) algorithm. The proposed approach is implemented in WEKA tool and analysed on experimental data extracted from open source software. Proposed work would firstly help the tester in identifying the low quality modules and then prioritize the test cases based on quality centric approach. The work also attempts to automate test case prioritization in object oriented testing. The results obtained are encouraging.

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“…Jaroenpiboonkit et al . proposed an approach to find a test order in order to use less stubs by using test dependence graph and object‐oriented slicing technique. They used test dependence graph to represent relationships among classes and object‐oriented slicing technique to break cyclic dependence of classes and to find a test order.…”

Section: Related Workmentioning

confidence: 99%

An approach of class integration test order determination based on test levels

Zhang

Jiang

Yuan

et al. 2014

Softw. Pract. Exper.

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In recent years, many approaches have been developed to determine the order of tested classes in interclass integration test. However, existing approaches are inaccurate, as they ignore the influence of abstract classes and polymorphism. In this paper, we propose a test-level-based approach to deal with class-integration-test order, in which both abstract classes and polymorphism are taken into account. First, based on interclass dependence analysis, we develop an edge-removing algorithm to eliminate cycles caused by static and dynamic dependencies, taking abstract classes and polymorphism into account. Then, after eliminating cycles, we propose a class-integration-test order algorithm based on test levels, including static and dynamic test levels. In this algorithm, we take into account the fact of some test levels infeasible caused by the characteristic of abstract classes that they cannot be instantiated and offer corresponding adjustment strategy. Finally, we design and implement a test level order generator. The experimental results show that the proposed strategy needs less test stubs than the most typically graph-based approaches.

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Finding a Test Order using Object-Oriented Slicing Technique

Cited by 7 publications

References 16 publications

Multi-objective optimization algorithms applied to the class integration and test order problem

Multi-objective optimization algorithms applied to the class integration and test order problem

Machine Learning based Test Case Prioritization in Object Oriented Testing

An approach of class integration test order determination based on test levels

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