An algorithm based on information retrieval that applies the lexical database WordNet together with a linear discriminant function is proposed. It calculates the degree of similarity between words and their relative importance to support the development of distributed applications based on web services. The algorithm uses the semantic information contained in the Web Service Description Language specifications and ranks web services based on their similarity to the one the developer is searching for. It is applied to a set of 48 real web services in five categories, then compared them to four other algorithms based on information retrieval, showing an averaged improvement over all data between 0.6% and 1.9% in precision and 0.7% and 3.1% in recall for the top 15 ranked web services. The objective was to reduce the burden and time spent searching web services during the development of distributed applications, and it can be used as an alternative to current web service discovery systems such as brokers in the Universal Description, Discovery, and Integration (UDDI) platform.

An algorithm based on semantic analysis of multiple detection tools’ reports using WordNet is proposed oriented on the refinement of code clones. It parses different detection tools’ reports looking for new clone specifications, and refines the location of existing ones using semantic information contained in source code. It is applied to a real and complex software system and is compared to three other well-known detection algorithms, discovering 4888 clone pairs more than the average detected by other tools; also making the code clones 3 lines longer (for a subset of the same system the results are proportional to the size reduction). The objective is to provide higher quantity of code clones, and more appropriated localization to be used in refactoring processes.