Recent advances in mechanical techniques for systematic testing have increased our ability to automatically find subtle bugs, and hence to deploy more dependable software. This paper builds on one such systematic technique, scope-bounded testing, to develop a novel specification-based approach for efficiently generating tests for products in a software product line. Given properties of features as firstorder logic formulas in Alloy, our approach uses SAT-based analysis to automatically generate test inputs for each product in a product line. To ensure soundness of generation, we introduce an automatic technique for mapping a formula that specifies a feature into a transformation that defines incremental refinement of test suites. Our experimental results using different data structure product lines show that an incremental approach can provide an order of magnitude speed-up over conventional techniques. We also present a further optimization using dedicated integer constraint solvers for feature properties that introduce integer constraints, and show how to use a combination of solvers in tandem for solving Alloy formulas.

We present a novel specification-based approach for generating tests for products in a software product line.

Recent advances in mechanical techniques for systematic testing have increased our ability to automatically find subtle bugs, and hence to deploy more dependable software. This paper builds on one such systematic technique, scope-bounded testing, to develop a novel specification-based approach for efficiently generating tests for products in a software product line. Given properties of features as firstorder logic formulas in Alloy, our approach uses SAT-based analysis to automatically generate test inputs for each product in a product line. To ensure soundness of generation, we introduce an automatic technique for mapping a formula that specifies a feature into a transformation that defines incremental refinement of test suites. Our experimental results using different data structure product lines show that an incremental approach can provide an order of magnitude speed-up over conventional techniques. We also present a further optimization using dedicated integer constraint solvers for feature properties that introduce integer constraints, and show how to use a combination of solvers in tandem for solving Alloy formulas.

Abstract. The declarative modeling language Alloy and its automatic analyzer provide an effective tool-set for building designs of systems and checking their properties. The Alloy Analyzer performs bounded exhaustive analysis using offthe-shelf SAT solvers. The analyzer's performance hinges on the complexity of the models and so far, its feasibility has been shown only within limited bounds. We present a novel optimization technique that defines program slicing for declarative models and enables efficient analyses exploiting partial solutions. We present an algorithm that computes transient slices for Alloy models by partitioning them into a base and a derived slice. A satisfying solution to the base slice is systematically extended to generate a solution for the entire model, while unsatisfiability of the base implies unsatisfiability of the entire model.By generating slices, our approach enables constraint prioritization, where the base slice assumes higher priority than the derived slice. Compared to the complete model, base and derived slices represent smaller and, ideally, simpler sub-problems, which, in turn, enables efficient analyses for the underlying SAT solvers. Our approach analyzes the structure of a given model and constructs a set of candidate slicing criteria. Our prototype tool, Kato, performs a small-scope analysis for each criterion to determine whether declarative slicing optimization provides any performance gain and, if so, to select a criterion that is likely to provide an optimal performance enhancement. The experimental results show that, with declarative slicing, it is possible to achieve significant improvements compared to the Alloy Analyzer.

This paper presents Kato, a

This paper presents a specification-based approach for systematic testing of products from a software product line. Our approach uses specifications given as formulas in Alloy, a first-order logic based on relations. Alloy formulas can be checked for satisfiability using the Alloy Analyzer. The fully automatic analyzer, given an Alloy formula and a scope, i.e., a bound on the universe of discourse, searches for an instance, i.e., a valuation to the relations in the formula such that it evaluates to true. The analyzer translates an Alloy formula (for the given scope) to a propositional formula and finds an instance using an off-the-shelf SAT solver. The use of an enumerating solver enables systematic test generation.We have developed a prototype based on the AHEAD theory. The prototype uses the recently developed Kodkod model finding engine of the Alloy Analyzer. We illustrate our approach using a data structure product line.

The declarative modeling language Alloy and its automatic analyzer provide an effective tool-set for building designs of systems and checking their properties. The Alloy Analyzer performs bounded exhaustive analysis using off-the-shelf SAT solvers. The analyzer's performance hinges on the complexity of the models and so far, its feasibility has been shown only within small bounds. With the growing popularity of analyzable declarative modeling languages, in general, and Alloy, in particular, it is imperative to develop new techniques that allow the underlying solvers to scale to real systems.We present Kato, a novel technique that defines program slicing for declarative models and enables efficient analyses using existing analyzers, such as the Alloy Analyzer. Given a declarative model, Kato identifies a slice, which represents the model's core: a satisfying solution to the slice can be systematically extended to generate a solution for the entire model, while unsatisfiability of the core implies unsatisfiability of the entire model. The experimental results show that it is possible to achieve a significant improvement in the solving time.

Abstract-We present aDeryaft, a novel technique for automating the writing of specifications in Alloy-a first-order relational logic with transitive closure. Alloy is particularly suitable for specifying structural properties of software, and has steadily been gaining popularity due to the rapid feedback that its SAT-based analyzer provides fully automatically. Alloy users however, still have to manually write specifications in a declarative language and use a paradigm that is different from the commonly used imperative programming paradigm.aDeryaft assists Alloy users in writing their specifications by providing a novel specification-writing approach, which is particularly tailored to users, such as engineers or practitioners in industry, who may not have much prior experience or proficiency in Alloy or similar logics. The user constructs by hand a few small concrete instances that represent the constraints of the software structure they want to specify. aDeryaft then fully automatically generates an executable Alloy specification, which represents the constraints that summarize the given structures. The generated specification is fully executable. For example, the Alloy Analyzer can use it to systematically enumerate a large number of concrete instances that satisfy the same constraints as the given instances. Indeed, a user may use aDeryaft to generate partial specifications while the user writes the rest of the specification by hand.To efficiently generate Alloy specifications, aDeryaft exploits the relational basis of Alloy and formulates graph properties that are likely to hold for the given instances. It then checks the properties for these instances and translates the valid properties into Alloy constraints, which it outputs as an Alloy specification. We illustrate aDeryaft's constraint generation on a variety of commonly used data structures.