Enrico Giunchiglia

Università degli Studi di Genova, Genova, Liguria, Italy

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Publications (152)54.78 Total impact

  • A. Khalili · M. Narizzano · A. Tacchella · E. Giunchiglia
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    ABSTRACT: In the context of structural testing, automatic test-pattern generation (ATPG) may fail to provide suites covering 100% of the testing requirements for grey-box programs, i.e., Applications wherein source code is available for some parts (white-box), but not for others (black-box). Furthermore, test suites based on abstract models may elicit behaviors on the actual program that diverge from the intended ones. In this paper, we present a new ATPG methodology to reduce divergence without increasing manual effort. This is achieved by (i) learning models of black-box components as finite-state machines, and (ii) composing the learnt models with the white-box components to generate test-suites for the grey-box program. Experiments with a prototypical implementation of our methodology show that it yields measurable improvements over two comparable state-of-the-art solutions.
    No preview · Article · Jul 2015

  • No preview · Conference Paper · Jan 2015

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  • No preview · Article · Jan 2015
  • Emanuele Di Rosa · Enrico Giunchiglia
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    ABSTRACT: The ability to effectively reason in the presence of qualitative preferences on literals or formulas is a central issue in Artificial Intelligence. In the last few years, two procedures have been presented in order to reason with propositional satisfiability (SAT) problems in the presence of additional, partially ordered qualitative preferences on literals or formulas: the first requires a modification of the branching heuristic of the SAT solver in order to guarantee that the first solution is optimal, while the second computes a sequence of solutions, each guaranteed to be better than the previous one. The two approaches have their own advantages and disadvantages and when compared on specific classes of instances – each having an empty partial order – the second seems to have superior performance. In this paper we show that the above two approaches for reasoning with qualitative preferences can be combined yielding a new effective procedure. In particular, in the new procedure we modify the branching heuristic – as in the first approach – by possibly changing the polarity of the returned literal, and then we continue the search – as in the second approach – looking for better solutions. We extended the experimental analysis conducted in previous papers by considering a wide variety of problems, having both an empty and a non-empty partial order: the results show that the new procedure performs better than the two previous approaches on average, and especially on the “hard” problems. As a preliminary result, we show that the framework of qualitative preferences on literals is more general and expressive than the framework on quantitative preferences.
    No preview · Article · Jan 2013 · Ai Communications
  • A. Armando · E. Giunchiglia · M. Maratea · S.E. Ponta
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    ABSTRACT: Business processes under authorization control are sets of coordinated activities subject to a security policy stating which agent can access which resource. Their behavior is difficult to predict due to the complex and unexpected interleaving of different execution flows within the process. Therefore, serious flaws may go undetected and manifest themselves only after deployment. This problem may be tackled by applying formal methods to reason about business process models. In this paper we outline the main contributions in this application domain of (Armando et al. 2012), that uses the action-based planning language C and the Causal Calculator tool CCALC. C is used to specify a business process from the banking domain that is representative of an important class of business processes of practical relevance, and proved to be a rich and natural formal specification language in this domain. CCALC is then used to automatically solve three reasoning tasks that arise in this context. We also compare C with the SMV specification language used in model-checking: the comparison highlights some key advantages of C in the business process domain. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.
    No preview · Article · Jan 2013
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    Full-text · Conference Paper · Jan 2013
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    Enrico Giunchiglia · Marco Maratea
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    ABSTRACT: Planning as Satisfiability (SAT) is the best approach for opt imally solving classical planning problems. The SAT-based planner SATPLAN has been the winner in the deterministic track for optimal planners i n the 4th International Planning Competition (IPC-4) and the co-winner in the last 5th IPC (together with another SAT-based planner). Given a planning problem �, SATPLAN works by (i) generating a SAT formulan with a fixed "makespan" n, and (ii) check- ingn for satisfiability. The algorithm stops ifn is satisfiable, and thus a plan has been found, otherwise n is increased. Despite its efficiency, and the optimality of the makespan, SATPLAN has signifi- cant deficiency related in particular to "plan quality", e.g ., the number of actions in the returned plan, and the possibility to express and reas on on "soft" goals. In this paper, we present SATPLAN≺, a system, modification of SATPLAN, which makes a significant step towards the elimination of SATPLAN's limitations. Given the optimal makespan, SATPLAN≺ returns plans with minimal number of actions and maximal number of satisfied "soft" goals, with respect to both cardinality and subset inclusions. We selected several benchmarks from different domains from all the IPCs: on these benchmarks we show that the plan quality returned by SATPLAN ≺ is often significantly higher than the one returned by SATPLAN. Quite surprisingly, this is often achieved without sacrific ing efficiency while ob- taining results that are competitive with the winning syste m of the "SimplePref- erences" domain in the satisfying track of the last IPC.
    Preview · Article · Mar 2012
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    ABSTRACT: Business processes under authorization control are sets of coordinated activities subject to a security policy stating which agent can access which resource. Their behavior is difficult to predict due to the complex and unexpected interleaving of different execution flows within the process. Serious flaws may thus go undetected and manifest themselves only after deployment. For this reason, business processes are being considered a new, promising application domain for formal methods and model checking techniques in particular. In this paper we show that action-based languages provide a rich and natural framework for the formal specification of and automated reasoning about business processes under authorization constraints. We do this by discussing the application of the action language C to the specification of a business process from the banking domain that is representative of an important class of business processes of practical relevance. Furthermore we show that a number of reasoning tasks that arise in this context (namely checking whether the control flow together with the security policy meets the expected security properties, building a security policy for the given business process under given security requirements, and finding an allocation of tasks to agents that guarantees the completion of the business process) can be carried out automatically using the Causal Calculator CCalc. We also compare C with the prominent specification language used in model-checking.1
    Full-text · Article · Jan 2012 · Journal of Computer and System Sciences
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    Enrico Giunchiglia · Marco Maratea
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    ABSTRACT: Planning as Satisfiability is one of the most well-known and effective techniques for classical planning: satplan has been the winning system in the deterministic track for optimal planners in the 4th International Planning Competition (IPC) and a cowinner in the 5th IPC. Given a planning problem П and a makespan n, the approach based on satisfiability (a.k.a. SAT-based) simply works by (i) constructing a SAT formula П n and (ii) checking Ðn for satisfiability: if there is a model for П n then we have found a plan, otherwise n is increased. The approach guarantees that the makespan is optimal, i.e. minimum. In this article we extend the Planning as Satisfiability approach in order to handle preferences and satplan in order to solve problems with simple preferences. This allows, e.g. to take into consideration ‘plan quality’ issues other than makespan, like number of actions and ‘soft’ goals. The basic idea is to explore the search space of possible plans in accordance with the given partially ordered preferences.We first prove that, at fixed makespan, our approach returns an ‘optimal’ plan, if any. Then, considering both classical planning problems and problems coming from IPC-5, we show that satplan extended in order to deal with preferences: (i) returns optimal plans that are often of considerable better quality, i.e. with fewer actions or with a better plan metric on soft goals, than satplan; and (ii) is overall competitive, in terms of plan quality, with sgplan, the winning system in the ‘SimplePreferences’ category of the IPC-5. Notably, such results are often obtained without sacrificing efficiency.
    Preview · Article · Apr 2011 · Journal of Logic and Computation
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    ABSTRACT: In this paper we present the parallel QBF Solver PaQuBE. This new solver leverages the additional computational power that can be exploited from modern computer architectures, from pervasive multi-core boxes to clusters and grids, to solve more relevant instances faster than previous generation solvers. Furthermore, PaQuBE's progressive MPI based parallel framework is the first to support advanced knowledge sharing in which solution cubes as well as conflict clauses can be exchanged between solvers. Knowledge sharing plays a critical role in the performance of PaQuBE. However, due to the overhead associated with sending and receiving MPI messages, and the restricted communication/network bandwidth available between solvers, it is essential to optimize not only what information is shared, but the way in which it is shared. In this context, we compare multiple conflict clause and solution cube sharing strategies, and finally show that an adaptive method provides the best overall results.
    No preview · Article · Jan 2011 · Fundamenta Informaticae
  • Emanuele Di Rosa · Enrico Giunchiglia · Barry O'Sullivan
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    ABSTRACT: Satisfiability problems with preferences enrich the expressive power of the Boolean Satisfiability problem (SAT) and facilitate the representation of qualitative/quantitative preferences on literals/formulas, defining an optimization problem. In some cases, it is not strictly necessary to compute an optimal solution, but it is enough to compute a sub-optimal solution of high quality and, possibly, provide a lower bound on the probability of finding an optimal solution. The 1/e - rule is the optimal stopping rule for the secretary problem that guarantees an optimal solution with probability at least 1/e can be found. In this paper: we show how to apply the 1/e-rule for solving satisfiability problems with preferences; we show that its theoretical success rate of about 37% is greater than 90% on random benchmarks; and, we show that the performance of the 1/e-rule on structured benchmarks is sometimes many orders-of-magnitude worse than that of complete search-based algorithms, and we explain the reasons why. We propose an algorithm based on the idea underlying the 1/e-rule, which needs the generation of just two solutions: the experimental evaluation shows that the average success rate of the proposed algorithm is a good approximation of the theoretical one of the 1/e-rule, since it is about 50.92% on 1956 structured problems and 48.33% on 2400 randomly generated instances with 200 variables.
    No preview · Conference Paper · Jan 2011
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    ABSTRACT: Testing and Bounded Model Checking (BMC) are two techniques used in Software Verification for bug-hunting. They are expression of two different philosophies: testing is used on the compiled code and it is more suited to find errors in common behaviors, while BMC is used on the source code to find errors in uncommon behaviors of the system. Nowadays, testing is by far the most used technique for software verification in industry: it is easy to use and even when no error is found, it can release a set of tests certifying the (partial) correctness of the compiled system. In the case of safety critical software, in order to increase the confidence of the correctness of the compiled system, it is often required that the provided set of tests covers 100% of the code. This requirement, however, substantially increases the costs associated to the testing phase, since it often involves the manual generation of tests. In this paper we show how BMC can be productively applied to the Software Verification process in industry. In particular, we show how to productively use a Bounded Model Checker for C programs (CBMC) as an automatic test generator for the Coverage Analysis of Safety Critical Software.
    No preview · Article · Dec 2010 · Journal of Automated Reasoning
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    Emanuele Di Rosa · Enrico Giunchiglia · Marco Maratea
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    ABSTRACT: Propositional satisfiability (SAT) is a success story in Computer Science and Artificial Intelligence: SAT solvers are currently used to solve problems in many different application domains, including planning and formal verification. The main reason for this success is that modern SAT solvers can successfully deal with problems having millions of variables. All these solvers are based on the Davis–Logemann–Loveland procedure (dll). In its original version, dll is a decision procedure, but it can be very easily modified in order to return one or all assignments satisfying the input set of clauses, assuming at least one exists. However, in many cases it is not enough to compute assignments satisfying all the input clauses: Indeed, the returned assignments have also to be “optimal” in some sense, e.g., they have to satisfy as many other constraints—expressed as preferences—as possible. In this paper we start with qualitative preferences on literals, defined as a partially ordered set (poset) of literals. Such a poset induces a poset on total assignments and leads to the definition of optimal model for a formula ψ as a minimal element of the poset on the models of ψ. We show (i) how dll can be extended in order to return one or all optimal models of ψ (once converted in clauses and assuming ψ is satisfiable), and (ii) how the same procedures can be used to compute optimal models wrt a qualitative preference on formulas and/or wrt a quantitative preference on literals or formulas. We implemented our ideas and we tested the resulting system on a variety of very challenging structured benchmarks. The results indicate that our implementation has comparable performances with other state-of-the-art systems, tailored for the specific problems we consider. KeywordsSatisfiability-Preferences
    Full-text · Article · Oct 2010 · Constraints
  • Enrico Giunchiglia · Paolo Marin · Massimo Narizzano
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    ABSTRACT: In this paper we present sQueezeBF, an effective preprocessor for QBFs that combines various techniques for eliminating variables and/or redundant clauses. In particular sQueezeBF combines (i) variable elimination via Q-resolution, (ii) variable elimination via equivalence substitution and (iii) equivalence breaking via equivalence rewriting. The experimental analysis shows that sQueezeBF can produce significant reductions in the number of clauses and/or variables - up to the point that some instances are solved directly by sQueezeBF - and that it can significantly improve the efficiency of a range of state-of-the-art QBF solvers - up to the point that some instances cannot be solved without sQueezeBF preprocessing.
    No preview · Conference Paper · Jul 2010
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    Enrico Giunchiglia · Marco Maratea
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    ABSTRACT: Planning as Satisfiability (SAT) is the best approach to opti-mally solve classical planning problems in term of makespan, as witnessed by the results of past International Planning Competitions (IPCs). The language of the IPCs has evolved in the last two editions in order to include plan quality mea-sures other than the makespan, e.g., to include "preferences" for the satisfaction of actions preconditions and/or goals: yet, the design, implementation and analysis of satisfiability-based approaches to cope with this issues is still at an early stage. In this paper, motivated by the recent availability of efficient systems to solve Pseudo-Boolean (PB) optimization problems, we present an approach to solve the instances in the "SimplePreferences" category of the IPC-5 by a reduc-tion to a PB formula, and then use off-the-shelf PB solvers. Our approach thus returns plans with optimal plan metrics, at fixed makespan. We prove that the approach is correct, and then show that an implementation of our ideas based on SATPLAN yields to an effective method to solve these IPC-5 benchmarks.
    Preview · Article · Jan 2010
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    Enrico Giunchiglia · Paolo Marin · Massimo Narizzano

    Full-text · Article · Jan 2010
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    Full-text · Conference Paper · Jan 2010
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    Enrico Giunchiglia · Marco Maratea
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    ABSTRACT: Planning as Satisfiability (SAT) is the best approach for optimally (wrt makespan) solving classical planning problems. SAT-based planners, like SAT- PLAN, can thus return plans having minimal makespan guaranteed. However, the returned plan does not take into account plan quality issues introduced in th e last two International Planning Competitions (IPCs): such issues include minimal- actions plans and plans with "soft" goals, where a metric has to be optimized over actions/goals. Recently, an approach to address such issues has been presented, in the framework of planning as satisfiability with preferences: by modifyin g the heuristic of the underlying SAT solver, the related system (called SATPLAN(P)) is guaranteed to return plans with minimal number of actions, or with maximal number of soft goals satisfied. But, besides such feature, it is well-kno wn that introducing ordering in SAT heuristics can lead to significant degradation in per- formances. In this paper, we present a generate-and-test approach to tackle the problem of dealing with such optimization issues: without imposing any ordering, a (candidate optimal) plan is first generated, and then a constraint is adde d impos- ing that the new plan (if any) has to be "better" than the last computed, i.e., the plan quality is increased at each iteration. We implemented this idea in SATPLAN, and compared the resulting systems wrt SATPLAN(P) and SGPLAN on planning problems coming from IPCs. The analysis shows performance benefi ts for the new approach, in particular on planning problems with many preferences.
    Preview · Conference Paper · Dec 2009

Publication Stats

4k Citations
54.78 Total Impact Points

Institutions

  • 1993-2013
    • Università degli Studi di Genova
      • • Dipartimento di Matematica (DIMA)
      • • Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS)
      Genova, Liguria, Italy
  • 2009
    • Ansaldo sts
      Genova, Liguria, Italy
  • 2005
    • University of Geneva
      Genève, Geneva, Switzerland
  • 1998
    • Università degli Studi di Trento
      • Department of Information Engineering and Computer Science
      Trento, Trentino-Alto Adige, Italy