Search

Security vulnerabilities are among the major concerns of modern software engineering. Successful results of machine learning techniques in various challenging applications have led to an emerging field of research to investigate the effectiveness of machine learning, and more recently, deep learning techniques, for the problem of software vulnerability analysis and discovery. In this paper, we explore the utilization of Graph Neural Networks as the latest trend and progress in the field of artificial neural networks. To this end, we propose an original neural vulnerability analysis approach, using customized intermediate graph representations of programs to train graph neural network models. Experimental results on a public suite of vulnerable programs show that the proposed approach is effective at the task of software vulnerability analysis. Additional empirical experiments answer complementary research questions about the proposed approach. In particular, we present experimental results for the challenging task of cross-project vulnerability analysis, with interesting insights on the capabilities of our novel approach. Furthermore, a software utility that was developed in the course of this study is also published as a further contribution to the research community. [ABSTRACT FROM AUTHOR]

Software systems tend to become larger and more complicated as the functional requirements increase gradually. Well-modularized software systems are widely believed to be understood, managed and evolved easily. Software module clustering problems (SMCPs) are significant and challenging problems in software engineering whose primary aim is to obtain perfect structures of software systems according to predefined rules. To solve SMCPs, it is important to design a measure which can appropriately evaluate the quality of the structures obtained. So far, the modularization quality (the basic MQ ) is widely used as the conventional measure. However, this measure does not consider global modules and edge directions between two modules. Therefore, in this paper, we design a new modularization quality measure based on the similarity, labeled as MS , to automatically guide optimization algorithms to find a good partition of software systems which considers both global modules and edge directions. To systematically validate the performance of MS , three optimization algorithms, namely hill-climbing algorithm (HC), genetic algorithm (GA), and multi-agent evolutionary algorithm (MAEA), are adopted to optimize MS on 17 real-world software systems with varying sizes. A thorough comparison shows that MS outperforms the basic MQ in guiding the optimization algorithms to find more meaningful module clusters for software systems. [ABSTRACT FROM AUTHOR]

Abstract: It is well known that data structures are essential in obtaining efficient algorithms, having a major importance in the software development process. A proper instantiation of a data structure can greatly impact the performance and the efficiency of the software system. In this paper we are focusing on the problem of customizing data structures instances during the execution of software systems using a supervised learning approach. In order to customize a data structure instance according to the software system’s current execution context, a neural network will be used. We experimentally evaluated our technique on a case study, emphasizing the advantages of the proposed approach. The obtained experimental results highlight the potential of our proposal in using a supervised learning based approach for dynamically configuring data structures instances. [Copyright &y& Elsevier]

Abstract: Pattern mining of cloned codes in software systems is a challenging task due to various modifications and the large size of software codes. Most existing approaches adopt a token-based software representation and use sequential analysis for pattern mining of cloned codes. Due to the intrinsic limitations of such spatial space analysis, these methods have difficulties handling statement reordering, insertion and control replacement. Recently, graph-based models such as program dependent graph have been exploited to solve these issues. Although they can improve the performance in terms of accuracy, they introduce additional problems. Their computational complexity is very high and dramatically increases with the software size, thus limiting their applications in practice. In this paper, we propose a novel pattern mining framework for cloned codes in software systems. It efficiently exploits software’s spatial space information as well as graph space information and thus can mine accurate patterns of cloned codes for software systems. Preliminary experimental results have demonstrated the superior performance of the proposed approach compared with other methods. [Copyright &y& Elsevier]

Abstract: The analysis of execution paths (also known as software traces) collected from a given software product can help in a number of areas including software testing, software maintenance and program comprehension. The lack of a scalable matching algorithm operating on detailed execution paths motivates the search for an alternative solution. This paper proposes the use of word entropies for the classification of software traces. Using a well-studied defective software as an example, we investigate the application of both Shannon and extended entropies (Landsberg–Vedral, Rényi and Tsallis) to the classification of traces related to various software defects. Our study shows that using entropy measures for comparisons gives an efficient and scalable method for comparing traces. The three extended entropies, with parameters chosen to emphasize rare events, all perform similarly and are superior to the Shannon entropy. [Copyright &y& Elsevier]

Abstract: The ripple effect metric shows what impact changes to a software will likely have on the rest of the system. In web-based data analysis, it has become a widespread practice to deploy both the script and the program developed with a high-level programming language software tool. Due to different vendors and the diversified philosophies behind different software tools, it may be slightly difficult to cope with the ripple effect across them. This paper initiates an experimental idea to minimize the wrapper interface ripple for web-based script tools and high-level programming environments and also indicates many potential research directions for the development of computationally intelligent tools in the software engineering domain that demand lower cost and less complexity. This work incorporates Ant Colony Optimization (ACO) and its prime artifact pheromone, which has been modified as a pheromone extension module to minimize ripples when cross-coding. A standard benchmark data set has been taken to validate the performance of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: Automatic software testing tools are still far from ideal for real world object-oriented (OO) software. The use of nature inspired search algorithms for this problem has been investigated recently. Testing complex data structures (e.g., containers) is very challenging since testing software with simple states is already hard. Because containers are used in almost every type of software, their reliability is of utmost importance. Hence, this paper focuses on the difficulties of testing container classes with nature inspired search algorithms. We will first describe how input data can be automatically generated for testing Java containers. Input space reductions and a novel testability transformation are presented to aid the search algorithms. Different search algorithms are then considered and studied in order to understand when and why a search algorithm is effective for a testing problem. In our experiments, these nature inspired search algorithms seem to give better results than the traditional techniques described in literature. Besides, the problem of minimising the length of the test sequences is also addressed. Finally, some open research questions are given. [Copyright &y& Elsevier]