Nicolas Gold

Nicolas Gold, Ross Purves

Computationally-intensive research (CIR) takes place on a wide variety of topics including AI. Its environmental impact is potentially significant yet it does not always fall clearly within the scope of organisational ethics review policy on its own merits. Many academic institutions have ethics oversight bodies (e.g. Research Ethics Committees or Institutional Review Boards) that occupy a potentially powerful position to encourage recognition of these issues and seek reflexive practice in researchers. However, policies are often poorly-defined in respect of environmental issues and thus research is not reviewed, reviewers have little guidance for legitimate critique, and researchers are not challenged to consider planetary limits on computing resources and the interaction of these with their research. This paper aims to address these problems by proposing scoping criteria for institutional ethics policy to bring CIR within the scope of ethics review on its own merits, framing evidential criteria for reviewers to apply in ethics review, and presenting a method by which CIR researchers can reflect on their proposed research in relation to environmental factors, and assess its potential value in the light of planetary limits.

Seongmin Lee, Dave Binkley, Robert Feldt et al.

We introduce Causal Program Dependence Analysis (CPDA), a dynamic dependence analysis that applies causal inference to model the strength of program dependence relations in a continuous space. CPDA observes the association between program elements by constructing and executing modified versions of a program. One advantage of CPDA is that this construction requires only light-weight parsing rather than sophisticated static analysis. The result is a collection of observations based on how often a change in the value produced by a mutated program element affects the behavior of other elements. From this set of observations, CPDA discovers a causal structure capturing the causal (i.e., dependence) relation between program elements. Qualitative evaluation finds that CPDA concisely expresses key dependence relationships between program elements. As an example application, we apply CPDA to the problem of fault localization. Using minimal test suites, our approach can rank twice as many faults compared to SBFL.

Paul Ralph, Nauman bin Ali, Sebastian Baltes et al.

Empirical Standards are natural-language models of a scientific community's expectations for a specific kind of study (e.g. a questionnaire survey). The ACM SIGSOFT Paper and Peer Review Quality Initiative generated empirical standards for research methods commonly used in software engineering. These living documents, which should be continuously revised to reflect evolving consensus around research best practices, will improve research quality and make peer review more effective, reliable, transparent and fair.