A Method for Automated Test Data Generation for Units using Classes of Qt Framework in C++ Projects

Bui, Thu Anh; Tung, Lam Nguyen; Tran, Hoang-Viet; Hùng, Phạm Ngọc

doi:10.1109/rivf55975.2022.10013869

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…It is also possible to check whether the expected boundary values described by the contracts are observed. In [50], the dynamic symbolic execution is used for the testing of C++ Qt Framework classes. A source code preprocessing phase is used to find constructors of Qt classes parameters.…”

Section: Program Execution Analysis Methodsmentioning

confidence: 99%

“…For the Java language, there are mostly search-based (6 papers, e.g., [76] or [79]) and then the machine-learning-based (3 papers - [80], [84], and [85]) and program execution analysis (3 papers - [43], [44], and [53]) methods. The program execution methods are prominent for the C/C++ programming languages (8 papers, e.g., [41] or [50]) and the data-description-based methods for the web applications (4 papers, e.g., [55] or [60]). The generally utilizable methods are mostly specification-(8 papers, e.g., [30] or [36]) and search-based (6 papers, e.g.…”

Section: Target Platformmentioning

confidence: 99%

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Current Trends in Automated Test Case Generation

Potuzak,

Lipka

2023

Annals of Computer Science and Information Systems

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The testing is an integral part of the software development. At the same time, the manual creation of individu-al test cases is a lengthy and error-prone process. Hence, an intensive research on automated test generation methods is ongoing for more than twenty years. There are many vastly dif-ferent approaches, which can be considered automated test case generation. However, a common feature is the generation of the data for the test cases. Ultimately, the test data decide the prog-ram branching and can be used on any testing level, starting with the unit tests and ending with the tests focused on the behavior of the entire application. The test data are also mostly independent on any specific technology, such as programming language or paradigm. This paper is a survey of existing litera-ture of the last two decades that deals with test data generation or with tests based on it. This survey is not a systematic literature review and it does not try to answer specific scientific questions formulated in advance. Its purpose is to map and categorize the existing methods and to summarize their common features. Such a survey can be helpful for any teams developing their methods for test data generation as it can be a starting point for the exploration of related work.Index terms-Software testing, test case generation, test data generation, papers survey.

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Section: Program Execution Analysis Methodsmentioning

confidence: 99%

Section: Target Platformmentioning

confidence: 99%

Current Trends in Automated Test Case Generation

Potuzak,

Lipka

2023

Annals of Computer Science and Information Systems

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“…After this preprocessing stage, a combination of data extraction techniques and classification methods is applied to detect source code vulnerabilities. Consequently, it can be observed that modern approaches to source code vulnerability detection typically focus on two main issues [6,7]: source code feature extraction methods and methods for predicting and classifying vulnerabilities based on these source code features.…”

Section: Introduction 1problemsmentioning

confidence: 99%

A New Framework for Software Vulnerability Detection Based on an Advanced Computing

Cong,

Xuan

2024

CMC

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The detection of software vulnerabilities written in C and C++ languages takes a lot of attention and interest today. This paper proposes a new framework called DrCSE to improve software vulnerability detection. It uses an intelligent computation technique based on the combination of two methods: Rebalancing data and representation learning to analyze and evaluate the code property graph (CPG) of the source code for detecting abnormal behavior of software vulnerabilities. To do that, DrCSE performs a combination of 3 main processing techniques: (i) building the source code feature profiles, (ii) rebalancing data, and (iii) contrastive learning. In which, the method (i) extracts the source code's features based on the vertices and edges of the CPG. The method of rebalancing data has the function of supporting the training process by balancing the experimental dataset. Finally, contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers away. The experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabilities using the Verum dataset. As a result, the method proposed in the article has brought a pretty good performance in all metrics, especially the Precision and Recall scores of 39.35% and 69.07%, respectively, proving the efficiency of the DrCSE Framework. It performs better than other approaches, with a 5% boost in Precision and a 5% boost in Recall. Overall, this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date.

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