Traditional forms of Crowdsourcing such as open source software development harness crowd contributions to democratize the creation of software. However, potential contributors must first overcome joining barriers forcing casually committed contributors to spend days or weeks onboarding and thereby reducing participation. To more effectively harness potential contributions from the crowd, we propose a method for programming in which work occurs entirely through microtasks, offering contributors short, self-contained tasks such as implementing part of a function or updating a call site invoking a function to match a change made to the function. In microtask programming, microtasks involve changes to a single artifact, are automatically generated as necessary by the system, and nurture quality through iteration. A study examining the feasibility of microtask programming to create small programs found that developers were able to complete 1008 microtasks, onboard and submit their first microtask in less than 15 minutes, complete all types of microtasks in less than 5 minutes on average, and create 490 lines of code and 149 unit tests. The results demonstrate the potential feasibility as well as revealing a number of important challenges to address to successfully scale microtask programming to larger and more complex programs.

Prior work and perception theory suggests that when exposed to discussion related to a particular piece of crowdsourced text content, readers generally perceive that content to be of lower quality than readers who do not see those comments, and that the effect is stronger if the comments display conflict. This paper presents a controlled experiment with over 1000 participants testing to see if this effect carries over to other documents from the same platform, including those with similar content or by the same author. Although we do generally find that perceived quality of the commented-on document is affected, effects do not carry over to the second item and readers are able to judge the second in isolation from the comment on the first. We confirm a prior finding about the negative effects conflict can have on perceived quality but note that readers report learning more from constructive conflict comments.

A key issue, whenever people work together to solve a complex problem, is how to divide the problem into parts done by different people and combine the parts into a solution for the whole problem. This paper presents a novel way of doing this with groups of contests called contest webs. Based on the analogy of supply chains for physical products, the method provides incentives for people to (a) reuse work done by themselves and others, (b) simultaneously explore multiple ways of combining interchangeable parts, and (c) work on parts of the problem where they can contribute the most.

Several intelligent technologies designed to improve navigability in and digestibility of text corpora use topic modeling such as the state-of-the-art Latent Dirichlet Allocation (LDA). This model and variants on it provide lower-dimensional document representations used in visualizations and in computing similarity between documents. This article contributes a method for validating such algorithms against human perceptions of similarity, especially applicable to contexts in which the algorithm is intended to support navigability between similar documents via dynamically generated hyperlinks. Such validation enables researchers to ground their methods in context of intended use instead of relying on assumptions of fit. In addition to the methodology, this article presents the results of an evaluation using a corpus of short documents and the LDA algorithm. We also present some analysis of potential causes of differences between cases in which this model matches human perceptions of similarity more or less well.

Programming work is inherently interdependent, requiring developers to share and coordinate decisions that crosscut the structure of code. This is particularly challenging for programming in a microtasking context, in which developers are assumed to be transient and thus cannot rely on traditional learning and coordination mechanisms such as an extended onboarding process and code ownership. In this paper, we explore scaffolding coordination and knowledge sharing through a question and answer system, structuring project knowledge and coordination into questions and answers. To investigate its potential for enabling coordination in a microtask setting, we implemented a Q&A system for microtask programming work and conducted a user study where a crowd used it to coordinate their work on a software project over a 30-hour period. The results reveal both the potential for the use of Q&A systems for within project coordination and challenges that this approach brings.

Microtask crowdsourcing organizes complex work into workflows, decomposing large tasks into small, relatively independent microtasks. Applied to software development, this model might increase participation in open source software development by lowering the barriers to contribu-tion and dramatically decrease time to market by increasing the parallelism in development work. To explore this idea, we have developed an approach to decomposing programming work into microtasks. Work is coordinated through tracking changes to a graph of artifacts, generating appropriate microtasks and propagating change notifications to artifacts with dependencies. We have implemented our approach in CrowdCode, a cloud IDE for crowd development. To evaluate the feasibility of microtask programming, we performed a small study and found that a small crowd of 12 workers was able to successfully write 480 lines of code and 61 unit tests in 14.25 person-hours of time.

Large-scale collaboration systems often separate their content from the deliberation around how that content was produced. Surfacing this deliberation may engender trust in the content generation process if the deliberation process appears fair, well-reasoned, and thorough. Alternatively, it could encourage doubts about content quality, especially if the process appears messy or biased. In this paper we report the results of an experiment where we found that surfacing deliberation generally led to decreases in perceptions of quality for the article under consideration, especially - but not only - if the discussion revealed conflict. The effect size depends on the type of editors' interactions. Finally, this decrease in actual article quality rating was accompanied by self-reported improved perceptions of the article and Wikipedia overall.