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Register a new userAn Empirical Study of Rule-Based and Learning-Based Approaches for Static Application Security Testing
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Added by
Roland Croft
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Background: Static Application Security Testing (SAST) tools purport to assist developers in detecting security issues in source code. These tools typically use rule-based approaches to scan source code for security vulnerabilities. However, due to the significant shortcomings of these tools (i.e., high false positive rates), learning-based approaches for Software Vulnerability Prediction (SVP) are becoming a popular approach. Aims: Despite the similar objectives of these two approaches, their comparative value is unexplored. We provide an empirical analysis of SAST tools and SVP models, to identify their relative capabilities for source code security analysis. Method: We evaluate the detection and assessment performance of several common SAST tools and SVP models on a variety of vulnerability datasets. We further assess the viability and potential benefits of combining the two approaches. Results: SAST tools and SVP models provide similar detection capabilities, but SVP models exhibit better overall performance for both detection and assessment. Unification of the two approaches is difficult due to lacking synergies. Conclusions: Our study generates 12 main findings which provide insights into the capabilities and synergy of these two approaches. Through these observations we provide recommendations for use and improvement.
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This demo paper presents the technical details and usage scenarios of $mu$SE: a mutation-based tool for evaluating security-focused static analysis tools for Android. Mutation testing is generally used by software practitioners to assess the robustness of a given test-suite. However, we leverage this technique to systematically evaluate static analysis tools and uncover and document soundness issues. $mu$SEs analysis has found 25 previously undocumented flaws in static data leak detection tools for Android. $mu$SE offers four mutation schemes, namely Reachability, Complex-reachability, TaintSink, and ScopeSink, which determine the locations of seeded mutants. Furthermore, the user can extend $mu$SE by customizing the API calls targeted by the mutation analysis. $mu$SE is also practical, as it makes use of filtering techniques based on compilation and execution criteria that reduces the number of ineffective mutations.
Mobile systems offer portable and interactive computing, empowering users, to exploit a multitude of context-sensitive services, including mobile healthcare. Mobile health applications (i.e., mHealth apps) are revolutionizing the healthcare sector by enabling stakeholders to produce and consume healthcare services. A widespread adoption of mHealth technologies and rapid increase in mHealth apps entail a critical challenge, i.e., lack of security awareness by end-users regarding health-critical data. This paper presents an empirical study aimed at exploring the security awareness of end-users of mHealth apps. We collaborated with two mHealth providers in Saudi Arabia to gather data from 101 end-users. The results reveal that despite having the required knowledge, end-users lack appropriate behaviour , i.e., reluctance or lack of understanding to adopt security practices, compromising health-critical data with social, legal, and financial consequences. The results emphasize that mHealth providers should ensure security training of end-users (e.g., threat analysis workshops), promote best practices to enforce security (e.g., multi-step authentication), and adopt suitable mHealth apps (e.g., trade-offs for security vs usability). The study provides empirical evidence and a set of guidelines about security awareness of mHealth apps.
ReTest is a novel testing tool for Java applications with a graphical user interface (GUI), combining monkey testing and difference testing. Since this combination sidesteps the oracle problem, it enables the generation of GUI-based regression tests. ReTest makes use of evolutionary computing (EC), particularly a genetic algorithm (GA), to optimize these tests towards code coverage. While this is indeed a desirable goal in terms of software testing and potentially finds many bugs, it lacks one major ingredient: human behavior. Consequently, human testers often find the results less reasonable and difficult to interpret. This thesis proposes a new approach to improve the initial population of the GA with the aid of machine learning (ML), forming an ML-technique enhanced-EC (MLEC) algorithm. In order to do so, existing tests are exploited to extract information on how human testers use the given GUI. The obtained data is then utilized to train an artificial neural network (ANN), which ranks the available GUI actions respectively their underlying GUI components at runtime---reducing the gap between manually created and automatically generated regression tests. Although the approach is implemented on top of ReTest, it can be easily used to guide any form of monkey testing. The results show that with only little training data, the ANN is able to reach an accuracy of 82% and the resulting tests represent an improvement without reducing the overall code coverage and performance significantly.
Flaky tests have gained attention from the research community in recent years and with good reason. These tests lead to wasted time and resources, and they reduce the reliability of the test suites and build systems they affect. However, most of the existing work on flaky tests focus exclusively on traditional unit tests. This work ignores UI tests that have larger input spaces and more diverse running conditions than traditional unit tests. In addition, UI tests tend to be more complex and resource-heavy, making them unsuited for detection techniques involving rerunning test suites multiple times. In this paper, we perform a study on flaky UI tests. We analyze 235 flaky UI test samples found in 62 projects from both web and Android environments. We identify the common underlying root causes of flakiness in the UI tests, the strategies used to manifest the flaky behavior, and the fixing strategies used to remedy flaky UI tests. The findings made in this work can provide a foundation for the development of detection and prevention techniques for flakiness arising in UI tests.
Being light-weight and cost-effective, IR-based approaches for bug localization have shown promise in finding software bugs. However, the accuracy of these approaches heavily depends on their used bug reports. A significant number of bug reports contain only plain natural language texts. According to existing studies, IR-based approaches cannot perform well when they use these bug reports as search queries. On the other hand, there is a piece of recent evidence that suggests that even these natural language-only reports contain enough good keywords that could help localize the bugs successfully. On one hand, these findings suggest that natural language-only bug reports might be a sufficient source for good query keywords. On the other hand, they cast serious doubt on the query selection practices in the IR-based bug localization. In this article, we attempted to clear the sky on this aspect by conducting an in-depth empirical study that critically examines the state-of-the-art query selection practices in IR-based bug localization. In particular, we use a dataset of 2,320 bug reports, employ ten existing approaches from the literature, exploit the Genetic Algorithm-based approach to construct optimal, near-optimal search queries from these bug reports, and then answer three research questions. We confirmed that the state-of-the-art query construction approaches are indeed not sufficient for constructing appropriate queries (for bug localization) from certain natural language-only bug reports although they contain such queries. We also demonstrate that optimal queries and non-optimal queries chosen from bug report texts are significantly different in terms of several keyword characteristics, which has led us to actionable insights. Furthermore, we demonstrate 27%--34% improvement in the performance of non-optimal queries through the application of our actionable insights to them.
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