DoKnowMe: Towards a Domain Knowledge-driven Methodology for Performance Evaluation

TL;DR

This paper introduces DoKnowMe, a domain knowledge-driven methodology for software performance evaluation, providing a flexible, instantiable framework validated in cloud services to improve evaluation consistency and effectiveness.

Contribution

The paper proposes a novel abstract evaluation methodology inspired by object-oriented concepts, enabling tailored performance evaluation approaches across different domains.

Findings

Validated in cloud services domain with positive results

Provides a flexible framework adaptable to various software systems

Achieves high usefulness, feasibility, effectiveness, and repeatability

Abstract

Software engineering considers performance evaluation to be one of the key portions of software quality assurance. Unfortunately, there seems to be a lack of standard methodologies for performance evaluation even in the scope of experimental computer science. Inspired by the concept of "instantiation" in object-oriented programming, we distinguish the generic performance evaluation logic from the distributed and ad-hoc relevant studies, and develop an abstract evaluation methodology (by analogy of "class") we name Domain Knowledge-driven Methodology (DoKnowMe). By replacing five predefined domain-specific knowledge artefacts, DoKnowMe could be instantiated into specific methodologies (by analogy of "object") to guide evaluators in performance evaluation of different software and even computing systems. We also propose a generic validation framework with four indicators (i.e.~usefulness, feasibility, effectiveness and repeatability), and use it to validate DoKnowMe in the Cloud services evaluation domain. Given the positive and promising validation result, we plan to integrate more common evaluation strategies to improve DoKnowMe and further focus on the performance evaluation of Cloud autoscaler systems.