Comprehensive Multiplatform Dynamic Program Analysis for Java and Android

Dynamic program analyses, such as profiling, tracing and bug-finding tools, are essential for software engineering. Unfortunately, implementing dynamic analyses for managed languages such as Java is unduly difficult and error-prone, because the run-time environments provide only complex low-level mechanisms. Currently, programmers writing custom tooling must expend great effort in tool development and maintenance, while still suffering substantial limitations such as incomplete code coverage or lack of portability. Ideally, a framework would be available in which dynamic analysis tools could be expressed at a high level, robustly, with high coverage and supporting alternative run-times such as Android. We describe our research on an \all-in-one" dynamic program analysis frame- work which uses a combination of techniques to satisfy these requirements

Fuzz Driver Generation

Poor software quality has led to tremendous costs and safety disasters, thus, software defects make the news with alarming regularity. Fuzzing is a bug detection technique. In particular, it is a software testing method where a stream of random input is sent to an application to stress the application and cause unexpected behaviour, resource leaks or crashes. When it comes to fuzzing software libraries, a fuzz driver plays an important role because it is the binder between the fuzzer and the target program. Traditionally fuzzing was used in closed-source platforms and also it is used to find vulnerabilities in kernels. However, recent developments show that fuzzing is now applied to open-source libraries. This research study analyses the role of a fuzz driver in the domain of fuzzing to recognise its importance, applications, techniques, challenges and future directions. This study intends to explore the state-of-the-art fuzz driver development strategies and identify trends in research and areas of potential improvements. We identified that fuzz driver generation is mainly seen as a minor activity in fuzzing research. It was evident that the development of a fuzz driver is laborious and time-consuming in nature but multiple innovative methodologies have been adopted in recent years to ease this task There are three main techniques to develop a fuzz driver: software developers manually writing a fuzz driver, semi-automatic generation of a fuzz driver through human-in-the-loop approaches and fully automatic generation of a fuzz driver. This research study evaluates these techniques through case studies and empirical analysis to recognise the best state-of-the-art fuzz driver generation strategy available for researchers and software testers. Our results show that manually developed fuzz drivers still outperform other methodologies in terms of performance but our results show how other methodologies could surpass their performance levels. Furthermore, this study analyses the effect of varying complexity levels of target functions on the performance of the fuzzing campaigns initiated through multiple fuzz driver generation techniques.Thesis (MPhil) -- University of Adelaide, School of Computer Science , 202

Analyzing Distributed Multi-platform Java and Android Applications with ShadowVM

In this tool demonstration, we present ShadowVM, a dynamic program analysis framework for Java and Android applications. ShadowVM offers a high-level programming model for expressing analyses, ensures complete bytecode coverage, and isolates the analysis from the observed application to avoid unwanted interference. An analysis implemented on top of ShadowVM can handle both Java and Android applications. First, we present and evaluate a simple code-coverage analysis implemented with ShadowVM. Second, we demonstrate the use of ShadowVM to analyze a distributed application comprising a Java server backend and an Android client frontend