Test Case Evaluation and Input Domain Reduction strategies for the Evolutionary Testing of Object-Oriented software

Ribeiro, José Carlos Bregieiro; Zenha-Rela, Mario; Vega, Francisco Fernández de

doi:10.1016/j.infsof.2009.06.009

Cited by 30 publications

(27 citation statements)

References 27 publications

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“…While several of these studies evaluate effectiveness of random testing in various scenarios [5,6,7,10,14,30,33], only the VPP study [30] represented. The only exceptions are predicate coverage used in the VPP study [30], statement coverage [5], MCDC [26], and an unspecified "structural coverage" [22,23]. We use not only predicate coverage, which subsumes branch coverage, but also mutation score.…”

Section: Related Workmentioning

confidence: 99%

“…The empirical evidence on research in this area often focuses on container classes [4,5,6,7,10,12,14,17,19,22,23,26,27,29,30,31,33,34]-containers are an important part of many standard libraries, and bugs in these containers could significantly affect applications, so directing testing efforts to these containers is worthwhile. Testing containers is not only important but also challenging to achieve with some advanced testing technique such as those based on symbolic execution [30].…”

Section: Introductionmentioning

confidence: 99%

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Testing Container Classes: Random or Systematic?

Sharma

Gligoric

Arcuri

et al. 2011

Fundamental Approaches to Software Engineering

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Abstract. Container classes such as lists, sets, or maps are elementary data structures common to many programming languages. Since they are a part of standard libraries, they are important to test, which led to research on advanced testing techniques targeting such containers and research on comparing testing techniques using such containers. However, these techniques have not been thoroughly compared to simpler techniques such as random testing. We present the results of a larger case study in which we compare random testing with shape abstraction, a systematic technique that showed the best results in a previous study. Our experiments show that random testing is about as effective as shape abstraction for testing these containers, which raises the question whether containers are well suited as a benchmark for comparing advanced testing techniques.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Testing Container Classes: Random or Systematic?

Sharma

Gligoric

Arcuri

et al. 2011

Fundamental Approaches to Software Engineering

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“…An Input Domain Reduction strategy, based on the concept of Purity Analysis, that meets this suggestion has already been proposed in [Ribeiro et al(2009)]. …”

Section: Adaptive Evolutionary Testingmentioning

confidence: 99%

“…Previous studies indicate that better results can be attained if the mutation operator is assigned a relatively high probability of selection [Ribeiro et al(2009)].…”

Section: Adaptive Evolutionary Testingmentioning

confidence: 99%

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Adaptive Evolutionary Testing: An Adaptive Approach to Search-Based Test Case Generation for Object-Oriented Software

Ribeiro

Zenha-Rela

Vega

2010

Nature Inspired Cooperative Strategies for Optimization (NICSO 2010)

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Adaptive Evolutionary Algorithms are distinguished by their dynamic manipulation of selected parameters during the course of evolving a problem solution; they have an advantage over their static counterparts in that they are more reactive to the unanticipated particulars of the problem. This paper proposes an adaptive strategy for enhancing Genetic Programming-based approaches to automatic test case generation. The main contribution of this study is that of proposing an Adaptive Evolutionary Testing methodology for promoting the introduction of relevant instructions into the generated test cases by means of mutation; the instructions from which the algorithm can choose are ranked, with their rankings being updated every generation in accordance to the feedback obtained from the individuals evaluated in the preceding generation. The experimental studies developed show that the adaptive strategy proposed improves the test case generation algorithm's efficiency considerably, while introducing a negligible computational overhead.

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Automatic assessment of object oriented programming assignments with unit testing in Python and a real case assignment

Climent

Arbeláez

2023

Comp Applic In Engineering

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In this paper, we focus on developing automatic assessment (AA) for a topic that has some difficulties in its practical assessment: object oriented programming (OOP). For evaluating that the OOP principles have been correctly applied to a real application, we use unit testing. In this paper, we focus on prioritizing that the students understand and apply correctly complex OOP principles and that they design properly the classes (including their relationships). In addition, we focus on the Python programming language rather than the typical previous works' focus in this area. Thus, we present a real case study of a practical assignment, in which the students have to implement characters for a video game. This assignment has the particularities and advantages that it is incremental and that it applies all four OOP principles within a single assignment. We also present its solution with the UML class diagram description. Furthermore, we provide unit testing for this case study and give general advice for generalizing the unit tests to other real case scenarios. Finally, we corroborate the effectiveness of our approach with positive student evaluations.

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Test Case Evaluation and Input Domain Reduction strategies for the Evolutionary Testing of Object-Oriented software

Cited by 30 publications

References 27 publications

Testing Container Classes: Random or Systematic?

Testing Container Classes: Random or Systematic?

Adaptive Evolutionary Testing: An Adaptive Approach to Search-Based Test Case Generation for Object-Oriented Software

Automatic assessment of object oriented programming assignments with unit testing in Python and a real case assignment

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