Minersoft: Searching software resourses in large - scale grid and cloud infrastructures

Abstract

Software retrieval is concerned with locating and identifying appropriate software resources to satisfy users requirements. It is considered to be one of the key technical issues in software reuse since \You must nd it before you can reuse it". In this thesis, we investigate the problem of supporting keyword-based searching for the discovery of software resources that are installed on the nodes of large-scale, federated Grid and Cloud computing infrastructures. We address a number of challenges that arise from the unstructured nature of software and the unavailability of software-related metadata on large-scale networked environments. We present Minersoft, a harvester that visits Grid/Cloud infrastructures, crawls their le-systems, identi es and classi es software resources, and discovers implicit associations between them. The results of Minersoft harvesting are encoded in a weighted, typed graph, named the Software Graph. A number of IR algorithms are used to enrich this graph with structural and content associations, to annotate software resources with keywords, and build inverted indexes to support keyword-based searching for software. Using a real testbed, we present an evaluation study of our approach, using data extracted from production-quality Grid and Cloud computing infrastructures. Experimental results show that Minersoft is a powerful tool for software retrieval.