Leveraging software search and reuse with automated software adaptation

Research on software reuse over the last decade has removed a lot of obstacles to its practical adoption. However, despite the claims in the software reuse literature of 1990's there are still some fundamental research challenges to be addressed, especially the problem of delivering “good” (i.e. high quality) search results with high precision and semantic recall. In terms of precision, one of the most promising approach to have emerged in recent years is test-driven search which only includes components in the result set that actually match a developers behavioral requirements as defined by a test case. However, the test-driven search prototypes available today currently have a low semantic recall because they are unable to find semantically matching components which have the wrong syntactic interface. In this paper we describe an automatic adaptation engine that alleviates this problem by automatically creating adapters to allow semantically mismatching components to be tested by test-driven search engines, thus significantly enhancing their semantic recall.