Test Data Generation of Bytecode by CLP Partial Evaluation

Abstract

Abstract. 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.

CPBPV: a constraint-programming framework for bounded program verification

“…But, when trityp=1, i==j holds and this incorrect version may answer that the triangle is isosceles while it may not be a triangle at all. For example, it will return 2 when (i,j,k)= (1,1,2). Table 4 depicts the experimental results.…”

Compositional CLP-Based Test Data Generation for Imperative Languages

“…In this section, we summarize the CLP-based approach to TDG for imperative languages introduced in [1] and recently extended to object-oriented languages with dynamic memory in [10]. For simplicity, we do not take aliasing of references into account and simplify the language by excluding inheritance and virtual invocations.…”

“…block-k) which limits to a threshold k the number of times we iterate on loops (resp. visit blocks in the control flow graph [1]). Among the wide variety of approaches to TDG (see [20]), our work focuses on glass-box testing, where testcases are obtained from the concrete program in contrast to black-box testing, where they are deduced from a specification of the program.…”

A dynamic constraint-based BMC strategy for generating counterexamples

“…Constraint Logic Programming (CLP) was used for test generation of programs (e.g., [16,17,18,1]) and provides a nice implementation tool extending symbolic execution techniques [6]. Gotlieb et al showed how to represent imperative programs as constraint logic programs: InKa [16] was a pioneer in the use of CLP for generating test data for C programs.…”

“…Instead of translating the program and the property into a big propositional formula and using a constraint solver at the last stage of BMC, constraint solving is integrated with state exploration to prune the state space as early as possible. More precisely, our strategy is based on the following observation: when the program is in an SSA-like form 1 , a faulty path can be built in a dynamic way. The Control Flow Graph (CFG) does not have to be explored in a top down (or bottom up) way, and compatible blocks can just be collected in a non-deterministic way.…”