Development of An Empirical Approach to Building Domain-Specific Knowledge Applied to High-End Computing

Abstract

This dissertation presents an empirical approach for building and storing knowledge about software engineering through human-subject research. It is based on running empirical studies in stages, where previously held hypotheses are supported or refuted in different contexts, and new hypotheses are generated. The approach is both mixed-methods based and opportunistic, and focuses on identifying a diverse set of potential sources for running studies. The output produced is an experience base which contains a set of these hypotheses, the empirical evidence which generated them, and the implications for practitioners and researchers. This experience base is contained in a software system which can be navigated by stakeholders to trace the "chain of evidence" of hypotheses as they evolve over time and across studies.

This approach has been applied to the domain of high-end computing, to build knowledge related to programmer productivity. The methods include controlled experiments and quasi-experiments, case studies, observational studies, interviews, surveys, and focus groups. The results of these studies have been stored in a proof-of-concept system that implements the experience base.