Communications - August 2008

a single aggregate score over a suite, this methodology is brittle because the result depends entirely on vagaries of the suite composition. ¹⁸ For example, while it is tempting to cite your best result—“we outperform X by up to 1000%”— stating an aggregate together with the best and worst results is more honest and insightful.

5. concLusion

Methodology plays a strategic role in experimental computer science research and development by creating a common ground for evaluating ideas and products. Sound methodology relies on relevant workloads, principled experimental design, and rigorous analysis. Evaluation methodology can therefore have a significant impact on a research field, potentially accelerating, retarding, or misdirecting energy and innovation. However, we work within a fast-changing environment and our methodologies must adapt to remain sound and relevant. Prompted by concerns among ourselves and others about the state of the art, we spent thousands of hours at eight institutions examining and addressing the problems of evaluating Java applications. The lack of direct funding, the perception that methodology is mundane, and the magnitude of the effort surely explain why these efforts are uncommon.

We address neglect of evaluation methodology concretely, in one domain at one point in time, and draw broader lessons for experimental computer science. The development and maintenance of the DaCapo benchmark suite and associated

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methodology have brought some much-needed improvement to our evaluations and to our particular field. However, experimental computer science cannot expect the upkeep of its methodological foundations to fall to ad hoc volunteer efforts. We encourage stakeholders such as industry and granting agencies to be forward-looking and make a systemic commitment to stem methodological neglect. Invest in the foundations of our innovation.

acknowledgments

We thank Andrew Appel, Randy Chow, Frans Kaashoek, and Bill Pugh, who encouraged this project at our three year NSF ITR review. We thank Mark Wegman, who initiated the public availability of Jikes RVM, and the developers of Jikes RVM. We gratefully acknowledge Fahad Gilani, who wrote the original version of the measurement infrastructure for his ANU Masters thesis; Xianglong Huang and Narendran Sachindran, who helped develop the replay methodology; and Jungwoo Ha and Magnus Gustafsson, who helped developed the multi-iteration replay methodology. We thank Tom Horn for his proofreading, and Guy Steele for his careful reading and suggestions.

This work was supported by NSF ITR CCR-0085792, CNS-0719966, NSF CCF-0429859, NSF EIA-0303609, DARPA F33615-03-C-4106, ARC DP0452011, ARC DP0666059, Intel, IBM, and Microsoft. Any opinions, findings and conclusions expressed herein are the authors’ and do not necessarily reflect those of the sponsors.

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