Category Archives: Science
[This article is cross-posted on PL Perspectives, the SIGPLAN blog.]
Even in this vibrant environment, my sense is that the size of the PL research community, and the impact of its research, is lower than it should be. My social network tells me that grad school applications signaling PL interest are declining, and while many researchers have won Turing awards for PL ideas these are becoming fewer and further between. Compare this state of affairs to that in the machine learning and security communities, which are growing rapidly in size and stature. Is there anything the PL community can do to increase the impact of its great work?
My recommendation is a concentrated effort to diversify PL research enthusiasts, and through them broaden the impact of PL-minded work.
We in PL can expand our tent. Education and outreach can help others to see that PL—its problems, methods, and ethos—is different and more exciting than they realized. We can lower the barrier to entry by engaging in a little housecleaning around expectations of core knowledge. We can also venture outside our tent, taking our knowledge and ideas to join other communities and address their problems. All of these steps will follow naturally from a focus on collaborative efforts attacking substantial problems, such as deployable AI or a quantum programming stack, the solution to which involves PL techniques, but many others besides. Continue reading
I was invited to give a talk at the Programming Languages Mentoring Workshop (PLMW) colocated with POPL’19 in January. The talk topic was What is programming languages research? I was excited to give this talk. It’s a topic I’ve thought a lot about over the years; in 2015 I wrote a blog post about it. Shortly thereafter I was elected SIGPLAN Chair, and over the ensuing three years came to know the exciting depth and breadth of the field even more deeply.
Like the blog post, the talk presents what I view as the goals, ethos, and benefits of PL research. Because the PLMW audience is senior undergraduates and early graduate students, the talk also presents an overview of PL as a field. In particular, it presents a tutorial of sorts of the areas and methods that PL researchers often develop and employ. To capture what these are, I skimmed a sampling of the conference proceedings of PLDI and POPL from the last 30 years. Doing so, I abstracted the “shape” of a PL research paper, and identified the broad areas PL researchers tend to focus on. The talk presents a flavor of these areas. Because the talk took place just before POPL, I focused most on topics that appear in POPL-published research; the talk highlights particular POPL’19 papers as examples.
Several people afterward told me that they enjoyed the talk and asked about whether a video of the talk might be available. Unfortunately, the talks were not recorded. SIGPLAN main conference talks are regularly video-recorded, but workshops and co-located events are hit and miss. I certainly understand the financial reasons for this situation. Nevertheless, it’s really too bad that PLMW talks are not recorded. In my experience, PLMW speakers put an exceptional amount of time and care into their talks, so they are often very well done. The talks also target a general audience, so they are potentially valuable to many more people than just those attending the actual event.
In the hopes that others might find it useful, I decided to video-record myself giving the PLMW talk. The recording is not great, but I hope that fact doesn’t get in the way of the conveying the content. If it does, maybe just the slide deck will prove useful. If you have comments or thoughts, I’m glad to hear them!
Many thanks to the organizers of PLMW@POPL’19 for a great event, and the opportunity to speak!
As we are gearing up for POPL’19, my students putting their talks together. For one of them, this is his first conference talk, and for the other it’s his first PL talk. So both of them separately asked me for advice about putting the talk together. After answering the second time, I realized I should follow Matt Might’s advice, and “reply to public” by sharing what I wrote to them.
There’s lots of good advice out there about making conference talks, which I summarize at the end. I think what I say here reinforces much of that good advice, and adds a bit to it.Continue reading
How do we know what we know? That question is the subject of study for the field of epistemology. Per Wikipedia, “Epistemology studies the nature of knowledge, justification, and the rationality of belief.” Science is one powerful means to knowledge. Per the linked Wikipedia article, “Science is viewed as a refined, formalized, systematic, or institutionalized form of the pursuit and acquisition of empirical knowledge.”
Most people are familiar with the basic scientific method: Pose a hypothesis about the world and then carry out an experiment whose empirical results can either support or falsify the hypothesis (and, inevitably, suggest additional hypotheses). In PL research we frequently rely on empirical evidence. Compiler optimizations, static and dynamic analyses, program synthesizers, testing tools, memory management algorithms, new language features, and other research developments each depend on some empirical evidence to demonstrate their effectiveness.
A key question for any experiment is: What is the standard of empirical evidence needed to adequately support the hypothesis?
This post is a summary of a paper co-authored by George T. Klees, Andrew Ruef, Benji Cooper, Shiyi Wei, and me that will appear in the Conference on Computer and Communications Security, this Fall, titled “Evaluating Fuzz Testing.”[ref]I also presented our work at the ISSISP’18 summer school, though the work has matured a bit since then.[/ref] It starts to answer the above question for research on fuzz testing (or simply, fuzzing), a process whose goal is to discover inputs that cause a program to crash. We studied empirical evaluations carried out by 32 research papers on fuzz testing. Looking critically at the evidence gathered by these papers, we find that no paper adheres to a sufficiently high standard of evidence to justify general claims of effectiveness (though some papers get close). We carry out our own experiments to illustrate why failure to meet the standard can produce misleading or incorrect conclusions.
Why have researchers systematically missed the mark here? I think the answer owes in part to the lack of an explicit standard of evidence. Our paper can be a starting point for such a standard for fuzz testing. More generally, several colleagues and I have been working on a checklist for empirical evaluations in PL/SE-style research. We welcome your feedback and participation!
I’ve served as Chair of ACM SIGPLAN for the last two years. It’s been a pleasure and a privilege to support the programming languages community, working with my fellow members on the SIGPLAN Executive Committee (EC). The current SIGPLAN EC is entering its third and final year of service. Elections for the next EC will be held in early 2018, and the newly elected members will begin serving in July of that year. Who will they be?
In this blog post I describe, in Q&A format, the activities and responsibilities of the SIGPLAN EC and its officers. My hope is that this post will inform possible volunteers about what they can expect to do if elected to the EC, and will help voters match candidates’ aptitudes to each position’s responsibilities. This post will also highlight some of the accomplishments of the current and past ECs, hopefully giving the community an idea of what we’ve been up to, on their behalf.
My colleague Emery Berger recently pointed me to the paper Single versus Double Blind Reviewing at WSDM 2017. This paper describes the results of a controlled experiment to test the impact of hiding authors’ identities during parts of the peer review process. The authors of the experiment—PC Chairs of the 2017 Web Search and Data Mining (WSDM’17) conference—examined the reviewing behavior of two sets of reviewers for the same papers submitted to the conference. They found that author identities were highly significant factors in a reviewer’s decision to recommend the paper be accepted to the conference. Both the fame of an author and the author’s affiliation were influential. Interestingly, whether the paper had a female author or not was not significant in recommendation decisions. [Update: a different look at the data found a penalty for female authors; see addendum to this post.]
I find this study very interesting, and incredibly useful. Many people I have talked to have suggested that we scientifically compare single- with double-blind reviewing (SBR vs. DBR, for short). A common idea is to run one version of a conference as DBR and compare its outcomes to a past version of the conference that used SBR. The problem with this approach is that both the papers under review and the people reviewing them would change between conference iterations. These are potentially huge confounding factors. While the WSDM’17 study is not perfect, it gets past some of these big issues.
In the rest of the post I will summarize the details of the WSDM’17 study and offer some thoughts about its strengths and weaknesses. I think we should attempt more studies like this for other conferences. Continue reading
While scientific papers were once available only to those willing to pay expensive fees to journal publishers, papers are now increasingly made available for free, as they enjoy some form of open access (OA). Not all forms of open access are the same, however. While the ACM SIGPLAN Executive Committee (of which I am the Chair) is generally happy with the OA rights SIGPLAN[ref]ACM SIGPLAN is the Association of Computing Machinery‘s Special Interest Group on Programming Languages.[/ref] authors currently enjoy, it may be time to push for even stronger rights. Reasonable people may disagree about the costs and benefits of such rights. As such, we would like your feedback (even if you are not a SIGPLAN member).
Please consider filling out this open access survey. It should take 5-10 minutes.
The remainder of this blog post discusses the issues in more depth. We invite your feedback!
Peer review is at the heart of the scientific process. As I have written about before, scientific results are deemed publishable by top journals and conferences only once they are given a stamp of approval by a panel of expert reviewers (“peers”). These reviewers act as a critical quality control, rejecting bogus or uninteresting results.
But peer review involves human judgment and as such it is subject to bias. One source of bias is a scientific paper’s authorship: reviewers may judge the work of unknown or minority authors more negatively, or judge the work of famous authors more positively, independent of the merits of the work itself.
The double-blind review process aims to mitigate authorship bias by withholding the identity of authors from reviewers. Unfortunately, simply removing author names from the paper (along with other straightforward prescriptions) may not be enough to prevent the reviewers from guessing who the authors are. If reviewers often guess and are correct, the benefits of blinding may not be worth the costs.
While I am a believer in double-blind reviewing, I have often wondered about its efficacy. So as part of the review process of CSF’16, I carried out an experiment:[ref]The structure of this experiment was inspired by the process Emery Berger put in place for PLDI’16, following a suggestion by Kathryn McKinley.[/ref] I asked reviewers to indicate, after reviewing a paper, whether they had a good guess about the authors of the paper, and if so to name the author(s). This post presents the results. In sum, reviewers often (2/3 of the time) had no good guess about authorship, but when they did, they were often correct (4/5 of the time). I think these results support using a double-blind process, as I discuss at the end.
Conferences are the heart of the PL research community. The best PL research is published at conferences, following a rigorous peer review process on par or better than the process of high-quality journals. Conferences are also where science gets done. As the respective community gathers to learn about the latest results, its members also network and interact, developing collaborations or carrying on projects that could produce the next breakthroughs. At conferences, students and young professors can rub elbows with luminaries, and researchers can develop problems and exchange ideas with practitioners.
One drawback of conferences (compared to other forms of research publication) is their cost. The monetary cost is obvious: at least one of a paper’s authors must pay to attend the conference, a cost that includes registration, travel, and hotel. Another cost that is less often considered is the environmental cost. In particular, I’m thinking of the impact that travel to/from the conference has on global warming. Most conference attendees travel great distances, and so travel by airplane. But air travel is particularly bad for global warming. So I wondered: what is the cost of conference travel, in terms of carbon footprint?
To get some idea, I decided to estimate the carbon footprint of the PLDI’16 program committee (PC) meeting, held just before and at the same venue as POPL’16. The result directly sheds some light on the carbon footprint of in-person PC meetings, and by treating it as a sample of the PL community overall, sheds light on the carbon footprint of PL conferences. In this blog post I present the results of my analysis and conclude with thoughts about possible actions to mitigate environmental cost.