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)

PET: A Partial Evaluation-based Test Case Generation Tool for Java Bytecode

PET is a prototype Partial Evaluation-based Test case generation tool for a subset of Java bytecode programs. It performs white-box test generation by means of two consecutive Partial Evaluations (PE). The first PE decompiles the Java bytecode program into an equivalent CLP (Constraint Logic Programming) counterpart. The second PE generates a test-case generator from the CLP program. This generator captures interesting test coverage criteria and it is able to generate further test cases on demand. For the first PE, PET incorporates an existing tool which decompiles bytecode to CLP. The main contribution of this work is the implementation of the second PE and the proof of concept of the approach. This has required the development of a partial evaluator for CLP with appropriate control strategies to ensure the required coverage criteria and to generate test-case generators. PET can be downloaded as free software from its web site, where a repository of examples and a web interface are also provided. Though PET has to be extended to be applicable to larger programs, we argue that it provides some evidence that the approach can be of practical interest

Verification of Java Bytecode using Analysis and Transformation of Logic Programs

State of the art analyzers in the Logic Programming (LP) paradigm are nowadays mature and sophisticated. They allow inferring a wide variety of global properties including termination, bounds on resource consumption, etc. The aim of this work is to automatically transfer the power of such analysis tools for LP to the analysis and verification of Java bytecode (JVML). In order to achieve our goal, we rely on well-known techniques for meta-programming and program specialization. More precisely, we propose to partially evaluate a JVML interpreter implemented in LP together with (an LP representation of) a JVML program and then analyze the residual program. Interestingly, at least for the examples we have studied, our approach produces very simple LP representations of the original JVML programs. This can be seen as a decompilation from JVML to high-level LP source. By reasoning about such residual programs, we can automatically prove in the CiaoPP system some non-trivial properties of JVML programs such as termination, run-time error freeness and infer bounds on its resource consumption. We are not aware of any other system which is able to verify such advanced properties of Java bytecode

Compositional CLP-based Test Data Generation for Imperative Languages

Glass-box test data generation (TDG) is the process of automatically generating test input data for a program by considering its internal structure. This is generally accomplished by performing symbolic execution of the program where the contents of variables are expressions rather than concrete values. The main idea in CLP-based TDG is to translate imperative programs into equivalent CLP ones and then rely on the standard evaluation mechanism of CLP to symbolically execute the imperative program. Performing symbolic execution on large programs becomes quickly expensive due to the large number and the size of paths that need to be explored. In this paper, we propose compositional reasoning in CLP-based TDG where large programs can be handled by testing parts (such as components, modules, libraries, methods, etc.) separately and then by composing the test cases obtained for these parts to get the required information on the whole program. Importantly, compositional reasoning also gives us a practical solution to handle native code, which may be unavailable or written in a different programming language. Namely, we can model the behavior of a native method by means of test cases and compositional reasoning is able to use the

Towards Compositional CLP-based Test Data Generation for Imperative Languages.

Test data generation (TDG) is the process of automatically generating test-cases for interesting test coverage criteria. The coverage criteria measure how well the program is exercised by a test suite. Examples of coverage criteria are: statement coverage which requires that each line of the code is executed; path coverage whic