Dynamic Analysis can be Improved with Automatic Test Suite Refactoring

Context: Developers design test suites to automatically verify that software meets its expected behaviors. Many dynamic analysis techniques are performed on the exploitation of execution traces from test cases. However, in practice, there is only one trace that results from the execution of one manually-written test case. Objective: In this paper, we propose a new technique of test suite refactoring, called B-Refactoring. The idea behind B-Refactoring is to split a test case into small test fragments, which cover a simpler part of the control flow to provide better support for dynamic analysis. Method: For a given dynamic analysis technique, our test suite refactoring approach monitors the execution of test cases and identifies small test cases without loss of the test ability. We apply B-Refactoring to assist two existing analysis tasks: automatic repair of if-statements bugs and automatic analysis of exception contracts. Results: Experimental results show that test suite refactoring can effectively simplify the execution traces of the test suite. Three real-world bugs that could previously not be fixed with the original test suite are fixed after applying B-Refactoring; meanwhile, exception contracts are better verified via applying B-Refactoring to original test suites. Conclusions: We conclude that applying B-Refactoring can effectively improve the purity of test cases. Existing dynamic analysis tasks can be enhanced by test suite refactoring

Inferring Concise Specifications of APIs

Modern software relies on libraries and uses them via application programming interfaces (APIs). Correct API usage as well as many software engineering tasks are enabled when APIs have formal specifications. In this work, we analyze the implementation of each method in an API to infer a formal postcondition. Conventional wisdom is that, if one has preconditions, then one can use the strongest postcondition predicate transformer (SP) to infer postconditions. However, SP yields postconditions that are exponentially large, which makes them difficult to use, either by humans or by tools. Our key idea is an algorithm that converts such exponentially large specifications into a form that is more concise and thus more usable. This is done by leveraging the structure of the specifications that result from the use of SP. We applied our technique to infer postconditions for over 2,300 methods in seven popular Java libraries. Our technique was able to infer specifications for 75.7% of these methods, each of which was verified using an Extended Static Checker. We also found that 84.6% of resulting specifications were less than 1/4 page (20 lines) in length. Our technique was able to reduce the length of SMT proofs needed for verifying implementations by 76.7% and reduced prover execution time by 26.7%

OpenJML: Software verification for Java 7 using JML, OpenJDK, and Eclipse

OpenJML is a tool for checking code and specifications of Java programs. We describe our experience building the tool on the foundation of JML, OpenJDK and Eclipse, as well as on many advances in specification-based software verification. The implementation demonstrates the value of integrating specification tools directly in the software development IDE and in automating as many tasks as possible. The tool, though still in progress, has now been used for several college-level courses on software specification and verification and for small-scale studies on existing Java programs.Comment: In Proceedings F-IDE 2014, arXiv:1404.578

Partial Evaluation of String Obfuscations for Java Malware Detection

The fact that Java is platform independent gives hackers the opportunity to write exploits that can target users on any platform, which has a JVM implementation. Metasploit is a well-known source of Javaexploits and to circumvent detection by Anti Virus (AV) software, obfuscation techniques are routinely applied to make an exploit more difficult to recognise. Popular obfuscation techniques for Java include stringobfuscation and applying reflection to hide method calls; two techniques that can either be used together or independently. This paper shows how to apply partial evaluation to remove these obfuscations and thereby improve AV matching. The paper presents a partial evaluator for Jimple, which is an intermediate language for JVM bytecode designed for optimisation and program analysis, and demonstrates how partially evaluated Jimple code, when transformed back into Java, improves the detection rates of a number of commercial AV products

How functional programming mattered

In 1989 when functional programming was still considered a niche topic, Hughes wrote a visionary paper arguing convincingly ‘why functional programming matters’. More than two decades have passed. Has functional programming really mattered? Our answer is a resounding ‘Yes!’. Functional programming is now at the forefront of a new generation of programming technologies, and enjoying increasing popularity and influence. In this paper, we review the impact of functional programming, focusing on how it has changed the way we may construct programs, the way we may verify programs, and fundamentally the way we may think about programs

Towards an Adaptive Skeleton Framework for Performance Portability

The proliferation of widely available, but very different, parallel architectures makes the ability to deliver good parallel performance on a range of architectures, or performance portability, highly desirable. Irregularly-parallel problems, where the number and size of tasks is unpredictable, are particularly challenging and require dynamic coordination. The paper outlines a novel approach to delivering portable parallel performance for irregularly parallel programs. The approach combines declarative parallelism with JIT technology, dynamic scheduling, and dynamic transformation. We present the design of an adaptive skeleton library, with a task graph implementation, JIT trace costing, and adaptive transformations. We outline the architecture of the protoype adaptive skeleton execution framework in Pycket, describing tasks, serialisation, and the current scheduler.We report a preliminary evaluation of the prototype framework using 4 micro-benchmarks and a small case study on two NUMA servers (24 and 96 cores) and a small cluster (17 hosts, 272 cores). Key results include Pycket delivering good sequential performance e.g. almost as fast as C for some benchmarks; good absolute speedups on all architectures (up to 120 on 128 cores for sumEuler); and that the adaptive transformations do improve performance

Can We Run in Parallel? Automating Loop Parallelization for TornadoVM

With the advent of multi-core systems, GPUs and FPGAs, loop parallelization has become a promising way to speed-up program execution. In order to stay up with time, various performance-oriented programming languages provide a multitude of constructs to allow programmers to write parallelizable loops. Correspondingly, researchers have developed techniques to automatically parallelize loops that do not carry dependences across iterations, and/or call pure functions. However, in managed languages with platform-independent runtimes such as Java, it is practically infeasible to perform complex dependence analysis during JIT compilation. In this paper, we propose AutoTornado, a first of its kind static+JIT loop parallelizer for Java programs that parallelizes loops for heterogeneous architectures using TornadoVM (a Graal-based VM that supports insertion of @Parallel constructs for loop parallelization). AutoTornado performs sophisticated dependence and purity analysis of Java programs statically, in the Soot framework, to generate constraints encoding conditions under which a given loop can be parallelized. The generated constraints are then fed to the Z3 theorem prover (which we have integrated with Soot) to annotate canonical for loops that can be parallelized using the @Parallel construct. We have also added runtime support in TornadoVM to use static analysis results for loop parallelization. Our evaluation over several standard parallelization kernels shows that AutoTornado correctly parallelizes 61.3% of manually parallelizable loops, with an efficient static analysis and a near-zero runtime overhead. To the best of our knowledge, AutoTornado is not only the first tool that performs program-analysis based parallelization for a real-world JVM, but also the first to integrate Z3 with Soot for loop parallelization

C++ const and Immutability: An Empirical Study of Writes-Through-const

The ability to specify immutability in a programming language is a powerful tool for developers, enabling them to better understand and more safely transform their code without fearing unintended changes to program state. The C++ programming language allows developers to specify a form of immutability using the const keyword. In this work, we characterize the meaning of the C++ const qualifier and present the ConstSanitizer tool, which dynamically verifies a stricter form of immutability than that defined in C++: it identifies const uses that are either not consistent with transitive immutability, that write to mutable fields, or that write to formerly-const objects whose const-ness has been cast away. We evaluate a set of 7 C++ benchmark programs to find writes-through-const, establish root causes for how they fail to respect our stricter definition of immutability, and assign attributes to each write (namely: synchronized, not visible, buffer/cache, delayed initialization, and incorrect). ConstSanitizer finds 17 archetypes for writes in these programs which do not respect our version of immutability. Over half of these seem unnecessary to us. Our classification and observations of behaviour in practice contribute to the understanding of a widely-used C++ language feature