2:00 - 2:20

• A Vision of Crowd Development

  Crowdsourcing has had extraordinary success in solving a diverse set of problems, ranging from digitization of libraries and translation of the Internet, to scientific challenges such as classifying elements in the galaxy or determining the 3D shape of an enzyme. By leveraging the power of the masses, it is feasible to complete tasks in mere days and sometimes even hours, and to take on tasks that were previously impossible because of their sheer scale. Underlying the success of crowdsourcing is a common theme — the microtask. By breaking down the overall task at hand into microtasks providing short, self-contained pieces of work, work can be performed independently, quickly, and in parallel — enabling numerous and often untrained participants to chip in. This paper puts forth a research agenda, examining the question of whether the same kinds of successes that microtask crowdsourcing is having in revolutionizing other domains can be brought to software development. That is, we ask whether it is possible to push well beyond the open source paradigm, which still relies on traditional, coarse-grained tasks, to a model in which programming proceeds through microtasks performed by vast numbers of crowd developers.

Thomas D. LaToza; André van der Hoek

University of California at Irvine, USA

2:20 - 2:40

• When App Stores Listen to the Crowd to Fight Bugs in the Wild

  App stores are digital distribution platforms that put available apps that run on mobile devices. Current stores are software repositories that deliver apps upon user requests. However, when an app has a bug, the store continues delivering defective apps until the developer uploads a fixed version, thus impacting on the reputation of both store and app developer. In this paper, we envision a new generation of app stores that: (a) reduce human intervention to maintain mobile apps, and (b) enhance store services with smart and autonomous functionalities to automatically increase the quality of the delivered apps. We sketch a prototype of our envisioned app store and we discuss the functionalities that current stores can enhance by incorporating automatic software repair techniques.

   

  Preprint available:

Maria Gomez; Matias Martinez; Martin Monperrus; Romain Rouvoy

INRIA, France; University of Lille 1, France

2:40 - 2:50

• Incorporating Human Intention into Self-Adaptive Systems

  Self-adaptive systems are fed with contextualinformation from the environments in which the systems operate,from within themselves, and from the users. Traditional selfadaptivesystems research has focused on inputs of systemsperformance, resources, exception, and error recovery that drivesystems' reaction to their environments. The intelligent ability ofthese self-adaptive systems is impoverished without knowledge ofa user's covert attention (thoughts, emotions, feelings). As aresult, it is difficult to build effective systems that anticipate andreact to users' needs as projected by covert behavior. This paperpresents the preliminary research results on capturing users'intention through neural input, and in reaction, commandingactions from software systems (e.g., load an application) based onhuman intention. Further, systems can self-adapt and refine theirbehaviors driven by such human covert behavior. The long-termresearch goal is to incorporate and synergize human neural input.Thus establishing software systems with a self-adaptive capabilityto “feel” and “anticipate” users intentions and put the human inthe loop.

Shihong Huang; Pedro Miranda

Florida Atlantic University, USA

2:50 - 3:00

• An Initiative to Improve Reproducibility and Empirical Evaluation of Software Testing Techniques

  The current concern regarding quality of evaluation performed in existing studies reveals the need for methods and tools to assist in the definition and execution of empirical studies and experiments. However, when trying to apply general methods from empirical software engineering in specific fields, such as evaluation of software testing techniques, new obstacles and threats to validity appears, hindering researchers' use of empirical methods. This paper discusses those issues specific for evaluation of software testing techniques and proposes an initiative for a collaborative effort to encourage reproducibility of experiments evaluating software testing techniques (STT). We also propose the development of a tool that enables automatic execution and analysis of experiments producing a reproducible research compendia as output that is, in turn, shared among researchers. There are many expected benefits from this Endeavour, such as providing a foundation for evaluation of existing and upcoming STT, and allowing researchers to devise and publish better experiments.

   

  Preprint available:

Francisco Gomes de Oliveira Neto; Richard Torkar; Patricia D. L. Machado

Universidade Federal de Campina Grande, Brazil; Chalmers University of Technology, Sweden; University of Gothenburg, Sweden

3:00 - 3:10

• Inferring Behavioral Specifications from Large-Scale Repositories by Leveraging Collective Intelligence

  Despite their proven benefits, useful, comprehensible, and efficiently checkable specifications are not widely available. This is primarily because writing useful, non-trivial specifications from scratch is too hard, time consuming, and requires expertise that is not broadly available. Furthermore, the lack of specifications for widely-used libraries and frameworks, caused by the high cost of writing specifications, tends to have a snowball effect. Core libraries lack specifications, which makes specifying applications that use them expensive. To contain the skyrocketing development and maintenance costs of high assurance systems, this self-perpetuating cycle must be broken. The labor cost of specifying programs can be significantly decreased via advances in specification inference and synthesis, and this has been attempted several times, but with limited success. We believe that practical specification inference and synthesis is an idea whose time has come. Fundamental breakthroughs in this area can be achieved by leveraging the collective intelligence available in software artifacts from millions of open source projects. Fine-grained access to such data sets has been unprecedented, but is now easily available. We identify research directions and report our preliminary results on advances in specification inference that can be had by using such data sets to infer specifications.

   

  Preprint available:

Hridesh Rajan; Tien N. Nguyen; Gary T. Leavens; Robert Dyer

Iowa State University, USA; University of Central Florida, USA; Bowling Green State University, USA

3:10 - 3:20

• Fast Feedback Cycles in Empirical Software Engineering Research

  Background/Context: Gathering empirical knowledge is a time consuming task and the results from empirical studies often are soon outdated by new technological solutions. As a result, the impact of empirical results on software engineering practice is often not guaranteed. Objective/Aim: In this paper, we summarize the ongoing discussion on ”Empirical Software Engineering 2.0” as a way to improve the impact of empirical results on industrial practices. We propose a way to combine data mining and analysis with domain knowledge to enable fast feedback cycles in empirical software engineering research. Method: We identify the key concepts on gathering fast feedback in empirical software engineering by following an experience-based line of reasoning by argument. Based on the identified key concepts, we design and execute a small proof of concept with a company to demonstrate potential benefits of the approach. Results: In our example, we observed that a simple double feedback mechanism notably increased the precision of the data analysis and improved the quality of the knowledge gathered. Conclusion: Our results serve as a basis to foster discussion and collaboration within the research community for a development of the idea.

Antonio Vetrò; Saahil Ognawala; Daniel Méndez Fernández; Stefan Wagner

Technische Universität München, Germany; University of Stuttgart, Germany