Tuesday, February 3, 2009

What I've Read This Week

Singer, J.; Vinson, N.G., "Ethical issues in empirical studies of software engineering," Software Engineering, IEEE Transactions on , vol.28, no.12, pp. 1171-1180, Dec 2002
URL: http://www.ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1158289&isnumber=25950

This paper presents a handful of ethical dilemmas that researchers who conduct empirical studies can get themselves into, along with advice on getting out or avoiding the situation all together.

  • What kings of studies could be create which contain no human subjects, but in which individuals can be identified (ie. from their source code)?
  • When can an employee's participation in an empirical study threaten their employment?
  • Is it possible to conduct a field study in which management doesn't know which of their employees are participating?
  • Should remuneration rates be adjusted to compete with a standard software engineer's salary?
  • Are raffles or draws valid replacements for remuneration? Does the exclusivity of the compensation (ie. only one subject wins the iPod) affect the data collected by the study? Will subjects 'try harder' in the task assigned if they think they may win a prize? Can prizes affect working relationships/situations after the researcher has left?
  • Does ACM Article 1.7 eliminate deceptive studies?
  • Regarding written concent/participation forms, does having a large number of anticipated uses of the data detract from a studies credability, and thereby make subjects less likely to participate?
John P. A. Ioannidis, "Why Most Published Research Findings Are False"
URL: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1182327

This paper describes a detailed statistical method (proof?) illustrating evidence that the majority of research papers published in this day and age go on to be refuted in the near future.
  • What is the 'power' the authors are referring to?
  • Is corollary 5 (corporations sponsoring research supress findings that they deem unfavorable for business reasons) just plain evil or misleading?
  • Null fields sound interesting. How do I tell if I'm stuck in a null field?
  • How do we determine R for a given field?

M. Jørgensen, and D. I. K. Sjøberg (2004) "Generalization and Theory Building in Software Engineering Research"
URL: http://simula.no/research/engineering/publications/SE.5.Joergensen.2004.c
  • Null hypotheses are a tell tale of (sometimes misused) statistical hypotheses testing. Should we as readers be concerned when we see clearly stated null hypotheses?
  • In their recommendations, the authors suggest that purely exploratory studies hold little or no value, given that vast amounts of knowledge concerning software engineering has been accumulated in other, older fields such as psychology. Although I agree that cross-disciplinary research is useful for SE, and many old ideas can be successfully applied in SE, I'm not sure I agree that there is no use in exploratory studies.
  • Proper definition of populations and subject sampling is important
  • It is difficult to transfer the results in one population to another. The most common example of this is performing a study on CS grad/undergrad students and expecting it to transfer to professionals. Is there any way we as CS grad students can perform studies that will be relevant to professionals, then?
Still working my way though RESTful Web Services. Just wrapped up the author's definition of ROA (resource oriented architecture). Very interesting. Hopefully this answers some questions brought up by my 2125 project.

Also on the stack are this paper about the Snowflock VM System and A Software Architecture Primer.

And, if there's time, I'll try to finish Ender's Game.

No comments: