Daniel Le Berre

Qualitative choice logic

Qualitative choice logic (QCL) is a propositional logic for representing alternative, ranked options for problem solutions. The logic adds to classical propositional logic a new connective called ordered disjunction: A ×→ B intuitively means: if possible A, but if A is not possible then at least B. The semantics of qualitative choice logic is based on a preference relation among models. Consequences of QCL theories can be computed through a compilation to stratified knowledge bases which in turn can be compiled to classical propositional theories. We also discuss potential applications of the logic, several variants of QCL based on alternative inference relations, and their relation to existing nonmonotonic formalisms.

The International SAT Solver Competitions

The International SAT Solver Competition is today an established series of competitive events aiming at objectively evaluating the progress in state-of-the-art procedures for solving Boolean satisfiability (SAT) instances. Over the years, the competitions have significantly contributed to the fast progress in SAT solver technology that has made SAT a practical success story of computer science. This short article provides an overview of the SAT solver competitions.

Automatic repair of buggy if conditions and missing preconditions with SMT

We present Nopol, an approach for automatically repairing buggy if conditions and missing preconditions. As input, it takes a program and a test suite which contains passing test cases modeling the expected behavior of the program and at least one failing test case embodying the bug to be repaired. It consists of collecting data from multiple instrumented test suite executions, transforming this data into a Satisfiability Modulo Theory (SMT) problem, and translating the SMT result -- if there exists one -- into a source code patch. Nopol repairs object oriented code and allows the patches to contain nullness checks as well as specific method calls.

Exploiting the real power of unit propagation lookahead

Abstract Abstract One of the best SAT solvers for random 3-SAT formulae, SATZ, is based on a heuristic called unit propagation lookahead (UPL). Unfortunately, it does not perform so well on specific structured instances, especially on the ones coming from an area where a huge interest for SAT has emerged in recent years: symbolic model checking (SMC). We claim that all the power of this heuristic is not used in SATZ, and that UPL can be extended to solve some real world structured problems, where the major competitors are using intelligent backtracking or specific deduction rules. We introduce a preprocessing technique that can be applied to simplify instances containing equivalent literals. This technique is based on UPL, so it can be easily added to any solver using this heuristic. We compare our approach to the new extension of SATZ for equivalency reasoning (EqSATZ) and another approach, the Stalmarck method, which is mainly used in SMC. The author would like to thank Yacine Boufkhad for his insights about C-SAT and 2-simplify, Mamoun Filali-Amine for pointing out the Stalmark method, Chu-Min Li for his discussions about EqSATZ and his implementation of the implied literal propagation in SATZ, Laurent Simon for his SAT-Ex web site, Miroslav Velev for his remarks on the early version of the paper and the referees for their helpful comments. This research was funded by an ARC Large Grant at The University of Newcastle.

The Essentials of the SAT 2003 Competition

The SAT 2003 Competition ran in February – May 2003, in conjunction with SAT’03 (the Sixth Fifth International Symposium on the Theory and Applications of Satisfiability Testing). One year after the SAT 2002 competition, it was not clear that significant progress could be made in the area in such a little time. The competition was a success – 34 solvers and 993 benchmarks, needing 522 CPU days – with a number of brand new solvers. Several 2003 competitors were even able to solve within 15mn benchmarks remained unsolved within 6 hours by 2002 competitors. We report here the essential results of the competition, interpret and statistically analyse them, and at last provide some suggestions for the future competitions.

Nopol: Automatic Repair of Conditional Statement Bugs in Java Programs

We propose Nopol, an approach to automatic repair of buggy conditional statements (i.e., if-then-else statements). This approach takes a buggy program as well as a test suite as input and generates a patch with a conditional expression as output. The test suite is required to contain passing test cases to model the expected behavior of the program and at least one failing test case that reveals the bug to be repaired. The process of Nopol consists of three major phases. First, Nopol employs angelic fix localization to identify expected values of a condition during the test execution. Second, runtime trace collection is used to collect variables and their actual values, including primitive data types and objected-oriented features (e.g., nullness checks), to serve as building blocks for patch generation. Third, Nopol encodes these collected data into an instance of a Satisfiability Modulo Theory (SMT) problem; then a feasible solution to the SMT instance is translated back into a code patch. We evaluate Nopol on 22 real-world bugs (16 bugs with buggy if conditions and six bugs with missing preconditions) on two large open-source projects, namely Apache Commons Math and Apache Commons Lang. Empirical analysis on these bugs shows that our approach can effectively fix bugs with buggy if conditions and missing preconditions. We illustrate the capabilities and limitations of Nopol using case studies of real bug fixes.

Using possibilistic logic for modeling qualitative decision: ATMS-based algorithms

This paper describes a logical machinery for computing decisions, where the available knowledge on the state of the world is described by a possibilistic propositional logic base (i.e., a collection of logical statements associated with qualitative certainty levels), and where the preferences of the user are also described by another possibilistic logic base whose formula weights are interpreted in terms of priorities. Two attitudes are allowed for the decision maker: a pessimistic risk-averse one and an optimistic one. The computed decisions are in agreement with a qualitative counterpart to the classical theory of expected utility, recently developed by three of the authors. A link is established between this logical view of qualitative decision making and an ATMS-based computation procedure. Efficient algorithms for computing pessimistic and optimistic optimal decisions are finally given in this logical setting (using some previous work of the fourth author).

Solving Linux Upgradeability Problems Using Boolean Optimization

Managing the software complexity of package-based systems can be regarded as one of the main challenges in software architectures. Upgrades are required on a short time basis and systems are expected to be reliable and consistent after that. For each package in the system, a set of dependencies and a set of conflicts have to be taken into account. Although this problem is computationally hard to solve, efficient tools are required. In the best scenario, the solutions provided should also be optimal in order to better fulfill users requirements and expectations. This paper describes two different tools, both based on Boolean satisfiability (SAT), for solving Linux upgradeability problems. The problem instances used in the evaluation of these tools were mainly obtained from real environments, and are subject to two different lexicographic optimization criteria. The developed tools can provide optimal solutions for many of the instances, but a few challenges remain. Moreover, it is our understanding that this problem has many similarities with other configuration problems, and therefore the same techniques can be used in other domains.

Dependency management for the eclipse ecosystem: eclipse p2, metadata and resolution

One of the strength of Eclipse, the well-known open platform for software development, is its extensibility made possible by the built-in pluggability mechanisms. However those pluggability mechanisms only reveal their full potential when extensions created by others are made easy to distribute and obtain. The purpose of Eclipse p2 project is to build a platform addressing the challenges of distribution and obtention of Eclipse and its extensions, which poses the same dependency management issues than for component based systems. This paper focuses on the dependency management aspect of p2. It describes the metadata used to express dependencies, the overall functioning of our resolver and a description of our propositional constraints based encoding. To conclude we describe the challenges to address in future releases.

Challenges in the QBF Arena: the SAT’03 Evaluation of QBF Solvers

The implementation of effective reasoning tools for deciding the satisfiability of Quantified Boolean Formulas (QBFs) is an important issue in several research fields such as Formal Verification, Planning, and Reasoning about Knowledge. Several QBF solvers have been implemented in the last few years, most of them extending the well-known Davis, Putnam, Logemann, Loveland procedure (DPLL) for propositional satisfiability (SAT). At the same time, a substantial breed of QBF benchmarks emerged, both in the form of statistical models for the generation of random formulas, and in the form of real-world instances. In this paper we report about the – first ever – evaluation of QBF solvers that was run as a joint event to SAT’03 Conference on Theory and Applications of Satisfiability Testing. Owing to the relative youngness of QBF tools and applications, we decided to run the comparison on a non-competitive basis, using the same technology that powered SAT’02 and SAT’03 competitions of SAT solvers. Running the evaluation enabled us to collect all sorts of data regarding the relative strength of different solvers and methods, the quality of the benchmarks, and to understand some of the current challenges for researchers involved in the QBF arena.