Automated Workarounds from Java Program Specifications based on SAT Solving

The failures that bugs in software lead to can sometimes be bypassed by the so-called workarounds: when a (faulty) routine fails, alternative routines that the system offers can be used in place of the failing one, to circumvent the failure. Existing approaches to workaround-based system recovery consider workarounds that are produced from equivalent method sequences, automatically computed from user-provided abstract models, or directly produced from user-provided equivalent sequences of operations. In this paper, we present two techniques for computing workarounds from Java code equipped with formal specifications, that improve previous approaches in two respects. First, the particular state where the failure originated is actively involved in computing workarounds, thus leading to repairs that are more state specific. Second, our techniques automatically compute workarounds on concrete program state characterizations, avoiding abstract software models and user-provided equivalences. The first technique uses SAT solving to compute a sequence of methods that is equivalent to a failing method on a specific failing state, but which can also be generalized to schemas for workaround reuse. The second technique directly exploits SAT to circumvent a failing method, building a state that mimics the (correct) behaviour of a failing routine, from a specific program state too. We perform an experimental evaluation based on case studies involving implementations of collections and a library for date arithmetic, showing that the techniques can effectively compute workarounds from complex contracts in an important number of cases, in time that makes them feasible to be used for run-time repairs. Our results also show that our state-specific workarounds enable us to produce repairs in many cases where previous workaround-based approaches are inapplicable.Fil: Uva, Marcelo Ariel. Universidad Nacional de Río Cuarto; ArgentinaFil: Ponzio, Pablo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Río Cuarto; ArgentinaFil: Regis, Germán. Universidad Nacional de Río Cuarto; ArgentinaFil: Aguirre, Nazareno Matias. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Río Cuarto; ArgentinaFil: Frias, Marcelo Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Instituto Tecnológico de Buenos Aires; Argentin

Chaining Test Cases for Reactive System Testing (extended version)

Testing of synchronous reactive systems is challenging because long input sequences are often needed to drive them into a state at which a desired feature can be tested. This is particularly problematic in on-target testing, where a system is tested in its real-life application environment and the time required for resetting is high. This paper presents an approach to discovering a test case chain---a single software execution that covers a group of test goals and minimises overall test execution time. Our technique targets the scenario in which test goals for the requirements are given as safety properties. We give conditions for the existence and minimality of a single test case chain and minimise the number of test chains if a single test chain is infeasible. We report experimental results with a prototype tool for C code generated from Simulink models and compare it to state-of-the-art test suite generators.Comment: extended version of paper published at ICTSS'1

Learning-Based Synthesis of Safety Controllers

We propose a machine learning framework to synthesize reactive controllers for systems whose interactions with their adversarial environment are modeled by infinite-duration, two-player games over (potentially) infinite graphs. Our framework targets safety games with infinitely many vertices, but it is also applicable to safety games over finite graphs whose size is too prohibitive for conventional synthesis techniques. The learning takes place in a feedback loop between a teacher component, which can reason symbolically about the safety game, and a learning algorithm, which successively learns an overapproximation of the winning region from various kinds of examples provided by the teacher. We develop a novel decision tree learning algorithm for this setting and show that our algorithm is guaranteed to converge to a reactive safety controller if a suitable overapproximation of the winning region can be expressed as a decision tree. Finally, we empirically compare the performance of a prototype implementation to existing approaches, which are based on constraint solving and automata learning, respectively

Conformant Planning as a Case Study of Incremental QBF Solving

We consider planning with uncertainty in the initial state as a case study of incremental quantified Boolean formula (QBF) solving. We report on experiments with a workflow to incrementally encode a planning instance into a sequence of QBFs. To solve this sequence of incrementally constructed QBFs, we use our general-purpose incremental QBF solver DepQBF. Since the generated QBFs have many clauses and variables in common, our approach avoids redundancy both in the encoding phase and in the solving phase. Experimental results show that incremental QBF solving outperforms non-incremental QBF solving. Our results are the first empirical study of incremental QBF solving in the context of planning and motivate its use in other application domains.Comment: added reference to extended journal article; revision (camera-ready, to appear in the proceedings of AISC 2014, volume 8884 of LNAI, Springer

Parallel local search for solving Constraint Problems on the Cell Broadband Engine (Preliminary Results)

We explore the use of the Cell Broadband Engine (Cell/BE for short) for combinatorial optimization applications: we present a parallel version of a constraint-based local search algorithm that has been implemented on a multiprocessor BladeCenter machine with twin Cell/BE processors (total of 16 SPUs per blade). This algorithm was chosen because it fits very well the Cell/BE architecture and requires neither shared memory nor communication between processors, while retaining a compact memory footprint. We study the performance on several large optimization benchmarks and show that this achieves mostly linear time speedups, even sometimes super-linear. This is possible because the parallel implementation might explore simultaneously different parts of the search space and therefore converge faster towards the best sub-space and thus towards a solution. Besides getting speedups, the resulting times exhibit a much smaller variance, which benefits applications where a timely reply is critical