27 research outputs found
A Rule-Learning Approach for Detecting Faults in Highly Configurable Software Systems from Uniform Random Samples
Software systems tend to become more and more configurable to satisfy the demands of their increasingly varied customers. Exhaustively testing the correctness of highly configurable software is infeasible in most cases because the space of possible configurations is typically colossal. This paper proposes addressing this challenge by (i) working with a representative sample of the configurations, i.e., a ``uniform'' random sample, and (ii) processing the results of testing the sample with a rule induction system that extracts the faults that cause the tests to fail. The paper (i) gives a concrete implementation of the approach, (ii) compares the performance of the rule learning algorithms AQ, CN2, LEM2, PART, and RIPPER, and (iii) provides empirical evidence supporting our procedure
Even shorter proofs without new variables
Proof formats for SAT solvers have diversified over the last decade, enabling
new features such as extended resolution-like capabilities, very general
extension-free rules, inclusion of proof hints, and pseudo-boolean reasoning.
Interference-based methods have been proven effective, and some theoretical
work has been undertaken to better explain their limits and semantics. In this
work, we combine the subsumption redundancy notion from (Buss, Thapen 2019) and
the overwrite logic framework from (Rebola-Pardo, Suda 2018). Natural
generalizations then become apparent, enabling even shorter proofs of the
pigeonhole principle (compared to those from (Heule, Kiesl, Biere 2017)) and
smaller unsatisfiable core generation.Comment: 21 page
Uniform and scalable SAT-sampling for configurable systems
Several relevant analyses on configurable software systems remain intractable because they require examining vast and highly-constrained configuration spaces. Those analyses could be addressed through statistical inference, i.e., working with a much more tractable sample that later supports generalizing the results obtained to the entire configuration space. To make this possible, the laws of statistical inference impose an indispensable requirement: each member of the population must be equally likely to be included in the sample, i.e., the sampling process needs to be "uniform". Various SAT-samplers have been developed for generating uniform random samples at a reasonable computational cost. Unfortunately, there is a lack of experimental validation over large configuration models to show whether the samplers indeed produce genuine uniform samples or not. This paper (i) presents a new statistical test to verify to what extent samplers accomplish uniformity and (ii) reports the evaluation of four state-of-the-art samplers: Spur, QuickSampler, Unigen2, and Smarch. According to our experimental results, only Spur satisfies both scalability and uniformity.Ministerio de Ciencia, Innovación y Universidades VITAL-3D DPI2016-77677-PMinisterio de Ciencia, Innovación y Universidades OPHELIA RTI2018-101204-B-C22Comunidad Autónoma de Madrid CAM RoboCity2030 S2013/MIT-2748Agencia Estatal de Investigación TIN2017-90644-RED
DeciLS-PBO: an Effective Local Search Method for Pseudo-Boolean Optimization
Local search is an effective method for solving large-scale combinatorial
optimization problems, and it has made remarkable progress in recent years
through several subtle mechanisms. In this paper, we found two ways to improve
the local search algorithms in solving Pseudo-Boolean Optimization (PBO):
Firstly, some of those mechanisms such as unit propagation are merely used in
solving MaxSAT before, which can be generalized to solve PBO as well; Secondly,
the existing local search algorithms utilize the heuristic on variables,
so-called score, to mainly guide the search. We attempt to gain more insights
into the clause, as it plays the role of a middleman who builds a bridge
between variables and the given formula. Hence, we first extended the
combination of unit propagation-based decimation algorithm to PBO problem,
giving a further generalized definition of unit clause for PBO problem, and
apply it to the existing solver LS-PBO for constructing an initial assignment;
then, we introduced a new heuristic on clauses, dubbed care, to set a higher
priority for the clauses that are less satisfied in current iterations.
Experiments on benchmarks from the most recent PB Competition, as well as three
real-world application benchmarks including minimum-width confidence band,
wireless sensor network optimization, and seating arrangement problems show
that our algorithm DeciLS-PBO has a promising performance compared to the
state-of-the-art algorithms