Test Data Generation of Bytecode by CLP Partial Evaluation

We employ existing partial evaluation (PE) techniques developed for Constraint Logic Programming (CLP) in order to automatically generate test-case generators for glass-box testing of bytecode. Our approach consists of two independent CLP PE phases. (1) First, the bytecode is transformed into an equivalent (decompiled) CLP program. This is already a well studied transformation which can be done either by using an ad-hoc decompiler or by specialising a bytecode interpreter by means of existing PE techniques. (2) A second PE is performed in order to supervise the generation of test-cases by execution of the CLP decompiled program. Interestingly, we employ control strategies previously defined in the context of CLP PE in order to capture coverage criteria for glass-box testing of bytecode. A unique feature of our approach is that, this second PE phase allows generating not only test-cases but also test-case generators. To the best of our knowledge, this is the first time that (CLP) PE techniques are applied for test-case generation as well as to generate test-case generators

Test Case Generation for Object-Oriented Imperative Languages in CLP

Testing is a vital part of the software development process. Test Case Generation (TCG) is the process of automatically generating a collection of test cases which are applied to a system under test. White-box TCG is usually performed by means of symbolic execution, i.e., instead of executing the program on normal values (e.g., numbers), the program is executed on symbolic values representing arbitrary values. When dealing with an object-oriented (OO) imperative language, symbolic execution becomes challenging as, among other things, it must be able to backtrack, complex heap-allocated data structures should be created during the TCG process and features like inheritance, virtual invocations and exceptions have to be taken into account. Due to its inherent symbolic execution mechanism, we pursue in this paper that Constraint Logic Programming (CLP) has a promising unexploited application field in TCG. We will support our claim by developing a fully CLP-based framework to TCG of an OO imperative language, and by assessing it on a corresponding implementation on a set of challenging Java programs. A unique characteristic of our approach is that it handles all language features using only CLP and without the need of developing specific constraint operators (e.g., to model the heap)

A Non-Null Annotation Inferencer for Java Bytecode

We present a non-null annotations inferencer for the Java bytecode language. We previously proposed an analysis to infer non-null annotations and proved it soundness and completeness with respect to a state of the art type system. This paper proposes extensions to our former analysis in order to deal with the Java bytecode language. We have implemented both analyses and compared their behaviour on several benchmarks. The results show a substantial improvement in the precision and, despite being a whole-program analysis, production applications can be analyzed within minutes

Badger: Complexity Analysis with Fuzzing and Symbolic Execution

Hybrid testing approaches that involve fuzz testing and symbolic execution have shown promising results in achieving high code coverage, uncovering subtle errors and vulnerabilities in a variety of software applications. In this paper we describe Badger - a new hybrid approach for complexity analysis, with the goal of discovering vulnerabilities which occur when the worst-case time or space complexity of an application is significantly higher than the average case. Badger uses fuzz testing to generate a diverse set of inputs that aim to increase not only coverage but also a resource-related cost associated with each path. Since fuzzing may fail to execute deep program paths due to its limited knowledge about the conditions that influence these paths, we complement the analysis with a symbolic execution, which is also customized to search for paths that increase the resource-related cost. Symbolic execution is particularly good at generating inputs that satisfy various program conditions but by itself suffers from path explosion. Therefore, Badger uses fuzzing and symbolic execution in tandem, to leverage their benefits and overcome their weaknesses. We implemented our approach for the analysis of Java programs, based on Kelinci and Symbolic PathFinder. We evaluated Badger on Java applications, showing that our approach is significantly faster in generating worst-case executions compared to fuzzing or symbolic execution on their own

A model-derivation framework for timing analysis of Java software Systems

One of the main challenges in developing a software system is to assure that its properties fulfill the specifications. In the context of this paper, we are especially interested in timing properties. Model-based software verification is one of the approaches to achieve this. However, model-based verification requires expressive models of software systems and deriving such models is not a trivial task. Although there are a few model derivation tool proposals for the purpose of model-checking timing properties, these are dedicated tools supporting a selected set of verification techniques and as such they are not explicitly designed for coping with new demands. This paper presents a framework that derives models from Java programs in an automated way for analyzing timing properties. The framework has the following properties that are not provided by the previous proposals: (1) Efficiency in model development, (2) consistency of models with software, (3) expressiveness of models, (4) scalability and (5) extensibility of the model derivation process

Symbolic PathFinder: Symbolic Execution of Java Bytecode

Symbolic Pathfinder (SPF) combines symbolic execution with model checking and constraint solving for automated test case generation and error detection in Java programs with unspecified inputs. In this tool, programs are executed on symbolic inputs representing multiple concrete inputs. Values of variables are represented as constraints generated from the analysis of Java bytecode. The constraints are solved using off-the shelf solvers to generate test inputs guaranteed to achieve complex coverage criteria. SPF has been used successfully at NASA, in academia, and in industry

An Overview of Ciao and its uses of DataLog for Program Analysis and Optimization

-Objectives: •Next-generation, high-level, multiparadigm programming language: Ciao. •Program development environments which perform, as part of compilation: Verification / debugging(i.e., detect bugs and offer guarantees of safety, reliability, and efficiency.) Optimization (optimized compilation, parallelization, ...)Using throughout techniques that are at the same time rigorous and practical. •Apply in a real system, with users –reality check! •Support also mainstream languages (e.g., Java / Java bytecode). - Several uses of Datalog and related techniques