Biblio

The need for cyber resilience is increasingly important in our technology-dependent society, where computing systems, devices and data will continue to be the target of cyber attackers. Hence, we propose a conceptual framework called ‘Human-in-the-Loop Explainable-AI-Enabled Vulnerability Detection, Investigation, and Mitigation’ (HXAI-VDIM). Specifically, instead of resolving complex scenario of security vulnerabilities as an output of an AI/ML model, we integrate the security analyst or forensic investigator into the man-machine loop and leverage explainable AI (XAI) to combine both AI and Intelligence Assistant (IA) to amplify human intelligence in both proactive and reactive processes. Our goal is that HXAI-VDIM integrates human and machine in an interactive and iterative loop with security visualization that utilizes human intelligence to guide the XAI-enabled system and generate refined solutions.

Over the last few years, researchers proposed a multitude of automated bug-detection approaches that mine a class of bugs that we call API misuses. Evaluations on a variety of software products show both the omnipresence of such misuses and the ability of the approaches to detect them. This work presents MuBench, a dataset of 89 API misuses that we collected from 33 real-world projects and a survey. With the dataset we empirically analyze the prevalence of API misuses compared to other types of bugs, finding that they are rare, but almost always cause crashes. Furthermore, we discuss how to use it to benchmark and compare API-misuse detectors.

Problem. Code migration between languages is challenging partly because different languages require developers to use different software libraries and frameworks. For example, in Java, Java Development Kit library (JDK) is a popular toolkit while .NET is the main framework used in C\# software development. Code migration requires not only the mappings between the language constructs (e.g., statements, expressions) but also the mappings among the APIs of the libraries/frameworks used in two languages. For example, in Java, to write to a file, one can use FileWriter.write of FileWriter, and in C\#, one can achieve the same function with StreamWriter.Write of StreamWriter. Such mapping is called API mapping.

Learning and remembering how to use APIs is difficult. While code-completion tools can recommend API methods, browsing a long list of API method names and their documentation is tedious. Moreover, users can easily be overwhelmed with too much information. We present a novel API recommendation approach that taps into the predictive power of repetitive code changes to provide relevant API recommendations for developers. Our approach and tool, APIREC, is based on statistical learning from fine-grained code changes and from the context in which those changes were made. Our empirical evaluation shows that APIREC correctly recommends an API call in the first position 59% of the time, and it recommends the correct API call in the top five positions 77% of the time. This is a significant improvement over the state-of-the-art approaches by 30-160% for top-1 accuracy, and 10-30% for top-5 accuracy, respectively. Our result shows that APIREC performs well even with a one-time, minimal training dataset of 50 publicly available projects.