Cowell* Background: The increasing use of ontologies highlights the need for a library for working with ontologies that is efficient, accessible from various programming languages, and compatible with common computational platforms. Results: We developed owlcpp, a library for storing and searching

consist of a set of programming constructs for writing speculativelyexecuted code and a compiler that translates code annotated with these constructs into speculatively-executable code that uses STM runtime libraries. Our approach uses annotations that simply mark the boundaries of the code

state-of-the-art code search tools. To investigate whether MAPO can assist programmers in programming tasks, we further conducted an empirical study. The results show that using MAPO, programmers produce code with fewer bugs when facing relatively complex API usages, comparing with using the two state

With high-end systems featuring multicore/multithreaded processors and high component density, power-aware high-performance multithreading libraries become a critical element of the system software stack. Online power and performance adaptation of multithreaded code from within user-level runtime

lead to certain problems that affect in producing optimal software. The problems such as reuse of existing libraries consumes more time, lack of knowledge on reusage of program interfaces and we can’t generate effective test inputs during white box testing. The first two problems reduce the software

Abstract. The process of understanding a source code in a high-level programming language involves complex computation. Given a piece of legacy code and a library of program plan templates, understanding the code corresponds to building mappings from parts of the source code to particular program

typical software project. In this paper, we describe how to combine this problem by combining program analysis methods with evolutionary computation to automatically repair bugs in off-the-shelf legacy C programs. Genetic programming (GP) is a computational method inspired by biological evolution which

software and hardware complexity grow. To make things even worse, the relative cost of man labor compared with that of machine computation increases rapidly. One approach to attacking the problem is automatic library generation via empirical evaluation. The essential idea is to have a meta-program

Algebraic algorithms deal with numbers, vectors, matrices, polynomials, for-mal power series, exponential and differential polynomials, rational functions, algebraic sets, curves and surfaces. In this vast area, manipulation with matri-ces and polynomials is fundamental for modern computations in Sciences and

We present an exact rational solver for mixed-integer linear programming which avoids the numerical inaccuracies inherent in the floating-point computations adopted in existing software. This allows the solver to be used for establishing fundamental theoretical results and in applications where