Our goal is to use the vast repositories of available open source code to generate specific functions or classes that meet a user’s specifications. The key words here are specifications and generate. We let users specify what they are looking for as precisely as possible using keywords, class or method signatures, test cases, contracts, and security constraints. Our system then uses an open set of program transformations to map retrieved code into what the user asked for. This approach is implemented in a prototype system for Java with a web interface. 1.

As statistical machine learning algorithms and techniques continue to mature, many researchers and developers see statistical machine learning not only as a topic of expert study, but also as a tool for software development. Extensive prior work has studied software development, but little prior work has studied software developers applying statistical machine learning. This paper presents interviews of eleven researchers experienced in applying statistical machine learning algorithms and techniques to human-computer interaction problems, as well as a study of ten participants working during a five-hour study to apply statistical machine learning algorithms and techniques to a realistic problem. We distill three related categories of difficulties that arise in applying statistical machine learning as a tool for software development: (1) difficulty pursuing statistical machine learning as an iterative and exploratory process, (2) difficulty understanding relationships between data and the behavior of statistical machine learning algorithms, and (3) difficulty evaluating the performance of statistical machine learning algorithms and techniques in the context of applications. This paper provides important new insight into these difficulties and the need for development tools that better support the application of statistical machine learning.

We present Sikuli, a visual approach to search and automation of graphical user interfaces using screenshots. Sikuli allows users to take a screenshot of a GUI element (such as a toolbar button, icon, or dialog box) and query a help system using the screenshot instead of ��� � ��������� � name. Sikuli also provides a visual scripting API for automating GUI interactions, using screenshot patterns to direct mouse and keyboard events. We report a web-based user study showing that searching by screenshot is easy to learn and

Although existing work has explored both information extraction and community content creation, most research has focused on them in isolation. In contrast, we see the greatest leverage in the synergistic pairing of these methods as two interlocking feedback cycles. This paper explores the potential synergy promised if these cycles can be made to accelerate each other by exploiting the same edits to advance both community content creation and learning-based information extraction. We examine our proposed synergy in the context of Wikipedia infoboxes and the Kylin information extraction system. After developing and refining a set of interfaces to present the verification of Kylin extractions as a non-primary task in the context of Wikipedia articles, we develop an innovative use of Web search advertising services to study people engaged in some other primary task. We demonstrate our proposed synergy by analyzing our deployment from two complementary perspectives: (1) we show we accelerate community content creation by using Kylin’s information extraction to significantly increase the likelihood that a person visiting a Wikipedia article as a part of some other primary task will spontaneously choose to help improve the article’s infobox, and (2) we show we accelerate information extraction by using contributions collected from people interacting with our designs to significantly improve Kylin’s extraction performance. ACM Classification:

This paper investigates the role of online resources in problem solving. We look specifically at how programmers—an exemplar form of knowledge workers—opportunistically interleave Web foraging, learning, and writing code. We describe two studies of how programmers use online resources. The first, conducted in the lab, observed participants ’ Web use while building an online chat room. We found that programmers leverage online resources with a range of intentions: They engage in just-in-time learning of new skills and approaches, clarify and extend their existing knowledge, and remind themselves of details deemed not worth remembering. The results also suggest that queries for different purposes have different styles and durations. Do programmers’ queries “in the wild ” have the same range of intentions, or is this result an artifact of the particular lab setting? We analyzed a month of queries to an online programming portal, examining the lexical structure, refinements made, and result pages visited. Here we also saw traits that suggest the Web is being used for learning and reminding. These results contribute to a theory of online resource usage in programming, and suggest opportunities for tools to facilitate online knowledge work.

The ready availability of online source-code examples has fundamentally changed programming practices. However, current search tools are not designed to assist with programming tasks and are wholly separate from editing tools. This paper proposes that embedding a task-specific search engine in the development environment can significantly reduce the cost of finding information and thus enable programmers to write better code more easily. This paper describes the design, implementation, and evaluation of Blueprint, a Web search interface integrated into the Adobe Flex Builder development environment that helps users locate example code. Blueprint automatically augments queries with code context, presents a code-centric view of search results, embeds the search experience into the editor, and retains a link between copied code and its source. A comparative laboratory study found that Blueprint enables participants to write significantly better code and find example code significantly faster than with a standard Web browser. Analysis of three months of usage logs with 2,024 users suggests that task-specific search interfaces can significantly change how and when people search the Web.

Jadeite is a new Javadoc-like API documentation system that takes advantage of multiple users ’ aggregate experience to reduce difficulties that programmers have learning new APIs. Previous studies have shown that programmers often guessed that certain classes or methods should exist, and looked for these in the API. Jadeite’s “placeholders ” let users add new “pretend ” classes or methods that are displayed in the actual API documentation, and can be annotated with the appropriate APIs to use instead. Since studies showed that programmers had difficulty finding the right classes from long lists in documentation, Jadeite takes advantage of usage statistics to display commonly used classes more prominently. Programmers had difficulty discovering how to instantiate objects, so Jadeite uses a large corpus of sample code to automatically the most common ways to construct an instance of any given class. An evaluation showed that programmers were about three times faster at performing common tasks with Jadeite than with standard Javadoc.

Interpreting compiler errors and exception messages is challenging for novice programmers. Presenting examples of how other programmers have corrected similar errors may help novices understand and correct such errors. This paper introduces HelpMeOut, a social recommender system that aids the debugging of error messages by suggesting solutions that peers have applied in the past. HelpMeOut comprises IDE instrumentation to collect examples of code changes that fix errors; a central database that stores fix reports from many users; and a suggestion interface that, given an error, queries the database for a list of relevant fixes and presents these to the programmer. We report on implementations of this architecture for two programming languages. An evaluation with novice programmers found that the technique can suggest useful fixes for 47 % of errors after 39 person-hours of programming in an instrumented environment.

When faced with the need for documentation, examples, bug fixes, error descriptions, code snippets, workarounds, templates, patterns, or advice, software developers frequently turn to their web browser. Web resources both organized and authoritative as well as informal and communitydriven are heavily used by developers. The time and attention devoted to finding (or re-finding) and navigating these sites is significant. We present Codetrail, a system that demonstrates how the developer’s use of web resources can be improved by connecting the Eclipse IDE and the Firefox web browser. Codetrail uses a communication channel and shared data model between these applications to implement a variety of integrative tools. By combining information previously available only to the IDE or the web browser alone (such as editing history, code contents, and recent browsing), Codetrail can automate previously manual tasks and enable new interactions that exploit the marriage of data and functionality from Firefox and Eclipse. Just as the IDE will change the contents of peripheral views to focus on the particular code or task with which the developer is engaged, so, too, the web browser can be focused on the developer’s current context and task. 1.

To build new tools and programming languages that make it easier for professional software developers to create, debug, and understand code, it is helpful to better understand the questions that developers ask during coding activities. We surveyed professional software developers and asked them to list hard-to-answer questions that they had recently asked about code. 179 respondents reported 371 questions. We then clustered these questions into 21 categories and 94 distinct questions. The most frequently reported categories dealt with intent and rationale – what does this code do, what is it intended to do, and why was it done this way? Many questions described very specific situations – e.g., what does the code do when an error occurs, how to refactor without breaking callers, or the implications of a specific change on security. These questions revealed opportunities for both existing research tools to help developers and for developing new languages and tools that make answering these questions easier.