Information Integration for Software Maintenance and Evolution.

Software maintenance and evolution is a particularly complex phenomenon in the case of long-lived, large-scale systems. It is not uncommon for such systems to progress through years of development history, a number of developers, and a multitude of software artifacts including millions of lines of code. Therefore, realizing even the slightest change may not always be straightforward. Clearly, changes are the central force driving software evolution. Therefore, it is not surprising that a significant effort has been (and should be) devoted in the software engineering community to systematically understanding, estimating, and managing changes to software artifacts. This effort includes the three core change related tasks of (1) expert developer recommendations - identifying who are the most experienced developers to implement needed changes, (2) traceability link recovery recovering dependencies (traceability links) between different types of software artifacts, and (3) software change impact analysis - which other software entities should be changed given a starting point.;This dissertation defines a framework for an integrated approach to support three core software maintenance and evolution tasks: expert developer recommendation, traceability link recovery, and software change impact analysis. The framework is centered on the use of conceptual and evolutionary relationships latent in structured and unstructured software artifacts. Information Retrieval (IR) and Mining Software Repositories (MSR) based techniques are used for analyzing and deriving these relationships. All the three tasks are supported under the framework by providing systematic combinations of MSR and IR analyses on single and multiple versions of a software system. Our approach to the integration of information is what sets it apart from previously reported relevant solutions in the literature. Evaluation on a number of open source systems suggests that such combinations do offer improvements over individual approaches