The Example Guru: Suggesting Examples to Novice Programmers in an Artifact-Based Context

Programmers in artifact-based contexts could likely benefit from skills that they do not realize exist. We define artifact-based contexts as contexts where programmers have a goal project, like an application or game, which they must figure out how to accomplish and can change along the way. Artifact-based contexts do not have quantifiable goal states, like the solution to a puzzle or the resolution of a bug in task-based contexts. Currently, programmers in artifact-based contexts have to seek out information, but may be unaware of useful information or choose not to seek out new skills. This is especially problematic for young novice programmers in blocks programming environments. Blocks programming environments often lack even minimal in-context support, such as auto-complete or in-context documentation. Novices programming independently in these blocks-based programming environments often plateau in the programming skills and API methods they use. This work aims to encourage novices in artifact-based programming contexts to explore new API methods and skills. One way to support novices may be with examples, as examples are effective for learning and highly available. In order to better understand how to use examples for supporting novice programmers, I first ran two studies exploring novices\u27 use and focus on example code. I used those results to design a system called the Example Guru. The Example Guru suggests example snippets to novice programmers that contain previously unused API methods or code concepts. Finally, I present an approach for semi-automatically generating content for this type of suggestion system. This approach reduces the amount of expert effort required to create suggestions. This work contains three contributions: 1) a better understanding of difficulties novices have using example code, 2) a system that encourages exploration and use of new programming skills, and 3) an approach for generating content for a suggestion system with less expert effort

Graphical scaffolding for the learning of data wrangling APIs

In order for students across the sciences to avail themselves of modern data streams, they must first know how to wrangle data: how to reshape ill-organised, tabular data into another format, and how to do this programmatically, in languages such as Python and R. Despite the cross-departmental demand and the ubiquity of data wrangling in analytical workflows, the research on how to optimise the instruction of it has been minimal. Although data wrangling as a programming domain presents distinctive challenges - characterised by on-the-fly syntax lookup and code example integration - it also presents opportunities. One such opportunity is how tabular data structures are easily visualised. To leverage the inherent visualisability of data wrangling, this dissertation evaluates three types of graphics that could be employed as scaffolding for novices: subgoal graphics, thumbnail graphics, and parameter graphics. Using a specially built e-learning platform, this dissertation documents a multi-institutional, randomised, and controlled experiment that investigates the pedagogical effects of these. Our results indicate that the graphics are well-received, that subgoal graphics boost the completion rate, and that thumbnail graphics improve navigability within a command menu. We also obtained several non-significant results, and indications that parameter graphics are counter-productive. We will discuss these findings in the context of general scaffolding dilemmas, and how they fit into a wider research programme on data wrangling instruction

Completely Customizing Modern GUIs Through Command-Driven Interfaces

An ideal user interface accommodates the requirements and abilities of its users, and every user has a specific set of needs that must be fulfilled in order for an interface to be useful. This thesis concentrates on using the post-deployment tailoring technique of customization in order to ensure that an interface meets a user's needs and abilities in a final, user-driven design step. To this end, the more entirely a UI can be customized, the more perfectly it can be transformed into a state that best suits its user. Very few systems offer complete customization: allowing the entirety of an interface to be customized, baring change to its interaction style. While a few systems do offer complete customization, no fully customizable system exists that is built using modern widget-based GUI's. This is the goal of the architecture described in this thesis, the Interface Manager. It uses interface building techniques to make cosmetic customizations and a command-driven style similar to that of Unix shells to make functionality customizations. This system allows interfaces to become well suited to their user, but it also offers open questions about user-initiated innovation in software and the scaling of visual interface design tools

The Essence of Software Engineering

Software Engineering; Software Development; Software Processes; Software Architectures; Software Managemen

Programming with human computation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 151-156).Amazon's Mechanical Turk provides a programmatically accessible micro-task market, allowing a program to hire human workers. This has opened the door to a rich field of research in human computation where programs orchestrate the efforts of humans to help solve problems. This thesis explores challenges that programmers face in this space: both technical challenges like managing high-latency, as well as psychological challenges like designing effective interfaces for human workers. We offer tools and experiments to overcome these challenges in an effort to help future researchers better understand and harness the power of human computation. The main tool this thesis offers is the crash-and-rerun programming model for managing high-latency tasks on MTurk, along with the TurKit toolkit which implements crash-and-rerun. TurKit provides a straightforward imperative programming environment where MTurk is abstracted as a function call. Based on our experience using TurKit, we propose a simple model of human computation algorithms involving creation and decision tasks. These tasks suggest two natural workflows: iterative and parallel, where iterative tasks build on each other and parallel tasks do not. We run a series of experiments comparing the merits of each workflow, where iteration appears to increase quality, but has limitations like reducing the variety of responses and getting stuck in local maxima. Next we build a larger system composed of several iterative and parallel workflows to solve a real world problem, that of transcribing medical forms, and report our experience. The thesis ends with a discussion of the current state-of-the-art of human computation, and suggests directions for future work.by Greg Little.Ph.D

Rethinking Productivity in Software Engineering

Get the most out of this foundational reference and improve the productivity of your software teams. This open access book collects the wisdom of the 2017 "Dagstuhl" seminar on productivity in software engineering, a meeting of community leaders, who came together with the goal of rethinking traditional definitions and measures of productivity. The results of their work, Rethinking Productivity in Software Engineering, includes chapters covering definitions and core concepts related to productivity, guidelines for measuring productivity in specific contexts, best practices and pitfalls, and theories and open questions on productivity. You'll benefit from the many short chapters, each offering a focused discussion on one aspect of productivity in software engineering. Readers in many fields and industries will benefit from their collected work. Developers wanting to improve their personal productivity, will learn effective strategies for overcoming common issues that interfere with progress. Organizations thinking about building internal programs for measuring productivity of programmers and teams will learn best practices from industry and researchers in measuring productivity. And researchers can leverage the conceptual frameworks and rich body of literature in the book to effectively pursue new research directions. What You'll Learn Review the definitions and dimensions of software productivity See how time management is having the opposite of the intended effect Develop valuable dashboards Understand the impact of sensors on productivity Avoid software development waste Work with human-centered methods to measure productivity Look at the intersection of neuroscience and productivity Manage interruptions and context-switching Who Book Is For Industry developers and those responsible for seminar-style courses that include a segment on software developer productivity. Chapters are written for a generalist audience, without excessive use of technical terminology. ; Collects the wisdom of software engineering thought leaders in a form digestible for any developer Shares hard-won best practices and pitfalls to avoid An up to date look at current practices in software engineering productivit

Architecture and implementation of online communities

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references.by Philip Greenspun.Ph.D

A Scholarship Approach to Model-Driven Engineering

Model-Driven Engineering is a paradigm for software engineering where software models are the primary artefacts throughout the software life-cycle. The aim is to define suitable representations and processes that enable precise and efficient specification, development and analysis of software. Our contributions to Model-Driven Engineering are structured according to Boyer\u27s four functions of academic activity - the scholarships of teaching, discovery, application and integration. The scholarships share a systematic approach towards seeking new insights and promoting progressive change. Even if the scholarships have their differences they are compatible so that theory, practice and teaching can strengthen each other.Scholarship of Teaching: While teaching Model-Driven Engineering to under-graduate students we introduced two changes to our course. The first change was to introduce a new modelling tool that enabled the execution of software models while the second change was to adapt pair lecturing to encourage the students to actively participate in developing models during lectures. Scholarship of Discovery: By using an existing technology for transforming models into source code we translated class diagrams and high-level action languages into natural language texts. The benefit of our approach is that the translations are applicable to a family of models while the texts are reusable across different low-level representations of the same model.Scholarship of Application: Raising the level of abstraction through models might seem a technical issue but our collaboration with industry details how the success of adopting Model-Driven Engineering depends on organisational and social factors as well as technical. Scholarship of Integration: Building on our insights from the scholarships above and a study at three large companies we show how Model-Driven Engineering empowers new user groups to become software developers but also how engineers can feel isolated due to poor tool support. Our contributions also detail how modelling enables a more agile development process as well as how the validation of models can be facilitated through text generation.The four scholarships allow for different possibilities for insights and explore Model-Driven Engineering from diverse perspectives. As a consequence, we investigate the social, organisational and technological factors of Model-Driven Engineering but also examine the possibilities and challenges of Model-Driven Engineering across disciplines and scholarships

Error Detection and Recovery in Software Development

Software rarely works as intended when it is first written. Software engineering research has long been concerned with assessing why software fails and who is to blame, or why a piece of software is flawed and how to prevent such faults in the future. Errors are examined in the context of bugs, elements of source code that produce undesirable, unexpected and unintended deviations in behaviour. Though error is a prevalent, mature topic within software engineering, error detection and recovery are less well understood. This research uses rich qualitative methods to study error detection and recovery in professional software development practice. It has considered conceptual representations of error in software engineering research and trade literature. Using ethnographic principles, it has gathered accounts given by professional developers in interviews and in video-recorded paired interaction. Developers performing a range of tasks were observed, and findings were compared to theories of human error formed in psychology and safety science. Three empirical studies investigated error from the perspective of developers, recon- structing the view they hold when errors arise, to build a catalogue of active encounters with error in conceptual design, at the desk and after the fact. Analyses were structured to consider development holistically over time, rather than in terms of discrete tasks. By placing emphasis on “local rationality”, analytical focus was redirected from outcomes toward factors that influence performance. The resultant observations are assembled in an account of error handling in software development as personal and situated (in time and the developer’s environment), with implications for the changing nature of expertise