A Survey of Constrained Combinatorial Testing

Combinatorial Testing (CT) is a potentially powerful testing technique, whereas its failure revealing ability might be dramatically reduced if it fails to handle constraints in an adequate and efficient manner. To ensure the wider applicability of CT in the presence of constrained problem domains, large and diverse efforts have been invested towards the techniques and applications of constrained combinatorial testing. In this paper, we provide a comprehensive survey of representations, influences, and techniques that pertain to constraints in CT, covering 129 papers published between 1987 and 2018. This survey not only categorises the various constraint handling techniques, but also reviews comparatively less well-studied, yet potentially important, constraint identification and maintenance techniques. Since real-world programs are usually constrained, this survey can be of interest to researchers and practitioners who are looking to use and study constrained combinatorial testing techniques

MicroRNA target prediction by constraint programming

MicroRNAs (miRNAs) are small regulatory RNAs of about 22 nucleotide long sequences that perform important functions such as larval development switches, cell proliferation and differentiation, apoptosis, fat metabolism, control of leaf and flower development. MicroRNA sequences are highly conserved across even unrelated species, a fact which suggests a key role in the evolutionary development. MicroRNAs are transcribed in the nucleus and perform their functions in the cytoplasm by binding to the complementary target mRNAs. MicroRNAs modulate gene expression either by suppressing translation or by mRNA cleavage and degradation. Plant microRNAs bind to their target mRNA on the coding region, almost perfectly, and perform their function by the cleavage of the mRNA, while animal microRNAs, bind imperfectly to their target mRNA, on the 3’ UTR region, and perform their functions by suppressing translation. MicroRNAs are discovered by both mutational studies and by computational methods. Hundreds of microRNAs have been cloned and sequenced in several organisms including humans, but to date, only few of them have known functions. The experimental techniques to understand the functions of miRNAs are time consuming and expensive which makes computational methods necessary. The identification of targets of plant microRNAs is straightforward due to near-perfect binding, but the imperfect binding of animal miRNAs to target mRNAs makes the computational target prediction rather difficult. In this thesis a new method is proposed for microRNA target prediction in animals using Constraint Logic Programming. With the established method a package micTar was developed to identify targets in Drosophila genome

Optimizing Airborne Area Surveillance Asset Placement

Currently there is no automated planning tool for the optimum positioning of USAF area surveillance assets for a theater level campaign. This research seeks to find the optimum or near optimum placement of the limited USAF airborne surveillance assets against a theater level target set. The problem of finding the optimum orbit points can be modeled as a classic maximal covering location problem (MCLP). Operational constraints on the placement of surveillance aircraft can be handled by preprocessing the potential orbit points to eliminate infeasible orbit points. Heavy emphasis is placed on preprocessing the data to reduce problem size and hence solution time. The aggregation of both the potential orbit points and targets was accomplished without loss of locational information. An existing heuristic was used to find a solution in a very short time. The heuristic finds the optimum orbit points for the available aircraft and any alternate solutions. Allocation decisions can then be accomplished

A constraint solver for software engineering : finding models and cores of large relational specifications

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 105-120).Relational logic is an attractive candidate for a software description language, because both the design and implementation of software often involve reasoning about relational structures: organizational hierarchies in the problem domain, architectural configurations in the high level design, or graphs and linked lists in low level code. Until recently, however, frameworks for solving relational constraints have had limited applicability. Designed to analyze small, hand-crafted models of software systems, current frameworks perform poorly on specifications that are large or that have partially known solutions. This thesis presents an efficient constraint solver for relational logic, with recent applications to design analysis, code checking, test-case generation, and declarative configuration. The solver provides analyses for both satisfiable and unsatisfiable specifications--a finite model finder for the former and a minimal unsatisfiable core extractor for the latter. It works by translating a relational problem to a boolean satisfiability problem; applying an off-the-shelf SAT solver to the resulting formula; and converting the SAT solver's output back to the relational domain. The idea of solving relational problems by reduction to SAT is not new. The core contributions of this work, instead, are new techniques for expanding the capacity and applicability of SAT-based engines. They include: a new interface to SAT that extends relational logic with a mechanism for specifying partial solutions; a new translation algorithm based on sparse matrices and auto-compacting circuits; a new symmetry detection technique that works in the presence of partial solutions; and a new core extraction algorithm that recycles inferences made at the boolean level to speed up core minimization at the specification level.by Emina Torlak.Ph.D