Emerging from the MIST: A Connector Tool for Supporting Programming by Non-programmers

Software development is an iterative process. As user re-quirements emerge software applications must be extended to support the new requirements. Typically, a programmer will add new code to an existing code base of an application to provide a new functionality. Previous research has shown that such extensions are easier when application logic is clearly separated from the user interface logic. Assuming that a programmer is already familiar with the existing code base, the task of writing the new code can be considered to be split into two sub-tasks: writing code for the application logic; that is, the actual functionality of the application; and writing code for the user interface that will expose the functionality to the end user. The goal of this research is to reduce the effort required to create a user interface once the application logic has been created, toward supporting scientists with minimal pro-gramming knowledge to be able to create and modify pro-grams. Using a Model View Controller based architecture, various model components which contain the application logic can be built and extended. The process of creating and extending the views (user interfaces) on these model components is simplified through the use of our Malleable Interactive Software Toolkit (MIST), a tool set an infrastructure intended to simplify the design and extension of dynamically reconfigurable interfaces. This paper focuses on one tool in the MIST suite, a connec-tor tool that enables the programmer to evolve the user interface as the application logic evolves by connecting related pieces of code together; either through simple drag-and-drop interactions or through the authoring of Python code. The connector tool exemplifies the types of tools in the MIST suite, which we expect will encourage collabora-tive development of applications by allowing users to inte-grate various components and minimizing the cost of de-veloping new user interfaces for the combined compo-nents

Stack Overflow in Github: Any Snippets There?

When programmers look for how to achieve certain programming tasks, Stack Overflow is a popular destination in search engine results. Over the years, Stack Overflow has accumulated an impressive knowledge base of snippets of code that are amply documented. We are interested in studying how programmers use these snippets of code in their projects. Can we find Stack Overflow snippets in real projects? When snippets are used, is this copy literal or does it suffer adaptations? And are these adaptations specializations required by the idiosyncrasies of the target artifact, or are they motivated by specific requirements of the programmer? The large-scale study presented on this paper analyzes 909k non-fork Python projects hosted on Github, which contain 290M function definitions, and 1.9M Python snippets captured in Stack Overflow. Results are presented as quantitative analysis of block-level code cloning intra and inter Stack Overflow and GitHub, and as an analysis of programming behaviors through the qualitative analysis of our findings.Comment: 14th International Conference on Mining Software Repositories, 11 page

The Role of Sonification as a Code Navigation Aid: Improving Programming Structure Readability and Understandability For Non-Visual Users

Integrated Development Environments (IDEs) play an important role in the workflow of many software developers, e.g. providing syntactic highlighting or other navigation aids to support the creation of lengthy codebases. Unfortunately, such complex visual information is difficult to convey with current screen-reader technologies, thereby creating barriers for programmers who are blind, who are nevertheless using IDEs. This dissertation is focused on utilizing audio-based techniques to assist non-visual programmers when navigating through large amounts of code. Recently, audio generation techniques have seen major improvements in their capabilities to covey visually-based information to both sighted and non-visual users – making them a potential candidate for providing useful information, especially in places where information is visually structured. However, there is little known about the usability of such techniques in software development. Therefore, we investigated whether audio-based techniques capable of providing useful information about the code structure to assist non-visual programmers. The major contributions in this dissertation are split into two major parts: The first part of this dissertation explains our prior work that investigates the major challenges in software development faced by non-visual programmers, specifically code navigation difficulties. It also discusses areas of improvement where additional features could be developed in order to make the programming environment more accessible to non-visual programmers. The second part of this dissertation focuses on studies aimed to evaluate the usability and efficacy of audio-based techniques for conveying the structure of the programming codebase, which was suggested by the stakeholders in Part I. Specifically, we investigated various sound effects, audio parameters, and different interaction techniques to determine whether these techniques could provide adequate support to assist non-visual programmers when navigating through lengthy codebases. In Part II, we discussed the methodological aspects of evaluating the above-mentioned techniques with the stakeholders and examine these techniques using an audio-based prototype that was designed to control audio timing, locations, and methods of interaction. A set of design guidelines are provided based on the evaluation described previously to suggest including an auditory-based feedback system in the programming environment in efforts to improve code structure readability and understandability for assisting non-visual programmers

Democratizing Web Automation: Programming for Social Scientists and Other Domain Experts

We have promised social scientists a data revolution, but it has not arrived. What stands between practitioners and the data-driven insights they want? Acquiring the data. In particular, acquiring the social media, online forum, and other web data that was supposed to help them produce big, rich, ecologically valid datasets. Web automation programming is resistant to high-level abstractions, so end-user programmers end up stymied by the need to reverse engineer website internals—DOM, JavaScript, AJAX. Programming by Demonstration (PBD) offered one promising avenue towards democratizing web automation. Unfortunately, as the web matured, the programs became too complex for PBD tools to synthesize, and web PBD progress stalled.This dissertation describes how I reformulated traditional web PBD around the insight that demonstrations are not always the easiest way for non-programmers to communicate their intent. By shifting from a purely Programming-By-Demonstration view to a Programming-By-X view that accepts a variety of user-friendly inputs, we can dramatically broaden the class of programs that come in reach for end-user programmers. Our Helena ecosystem combines (i) usable PBD-based program drafting tools, (ii) learnable programming languages, and (iii) novel programming environment interactions. The end result: non-coders write Helena programs in 10 minutes that can handle the complexity of modern webpages, while coders attempt the same task and time out in an hour. I conclude with a discussion of the abstraction-resistant domains that will fall next and how hybrid PL-HCI breakthroughs will vastly expand access to programming

Translation into any natural language of the error messages generated by any computer program

Since the introduction of the Fortran programming language some 60 years ago, there has been little progress in making error messages more user-friendly. A first step in this direction is to translate them into the natural language of the students. In this paper we propose a simple script for Linux systems which gives word by word translations of error messages. It works for most programming languages and for all natural languages. Understanding the error messages generated by compilers is a major hurdle for students who are learning programming, particularly for non-native English speakers. Not only may they never become "fluent" in programming but many give up programming altogether. Whereas programming is a tool which can be useful in many human activities, e.g. history, genealogy, astronomy, entomology, in many countries the skill of programming remains confined to a narrow fringe of professional programmers. In all societies, besides professional violinists there are also amateurs. It should be the same for programming. It is our hope that once translated and explained the error messages will be seen by the students as an aid rather than as an obstacle and that in this way more students will enjoy learning and practising programming. They should see it as a funny game.Comment: 14 pages, 1 figur

Easing IoT Development for Novice Programmers Through Code Recipes

The co-existence of various kinds of devices, protocols, architectures, and programming languages make Internet of Things (IoT) systems complex to develop, even for experienced programmers. Perforce, Software Engineering challenges are even more difficult to address by novice programmers. Previous research focused on identifying the most challenging issues that novice programmers experience when developing IoT systems. The results suggested that the integration of heterogeneous software components resulted one of the most painful issues, mainly due to the lack of documentation understandable by inexperienced developers, from both conceptual and technical perspectives. In fact, novice programmers devote a significant effort looking for documentation and code samples willing to understand them conceptually, or in the worst case, at least to make them work. Driven by the research question: "How do the lessons learned by IoT novice programmers can be captured, so they become an asset for other novice developers?", in this paper, we introduce Code Recipes. They consist of summarized and well-defined documentation modules, independent from programming languages or run-time environments, by which non-expert programmers can smoothly become familiar with source code, written by other developers that faced similar issues. Through a use case, we show how Code Recipes are a feasible mechanism to support novice IoT programmers in building their IoT system

A Graph Rewriting Visual Language for Database Programming

Textual database programming languages are computationally complete, but have the disadvantage of giving the user a non-intuitive view of the database information that is being manipulated. Visual languages developed in recent years have allowed naive users access to a direct representation of data, often in a graph form, but have concentrated on user interface rather than complex programming tasks. There is a need for a system which combines the advantages of both these programming methods. We describe an implementation of Spider, an experimental visual database programming language aimed at programmers. It uses a graph rewriting paradigm as a basis for a fully visual, computationally complete language. The graphs it rewrites represent the schema and instances of a database. The unique graph rewriting method used by Spider has syntactic and semantic simplicity. Its form of algorithmic expression allows complex computation to be easily represented in short programs. Furthermore, Spider has greater power than normally provided in textual systems, and we show that queries on the schema and associative queries can be performed easily and without requiring any additions to the language

Block-Based Development of Mobile Learning Experiences for the Internet of Things

The Internet of Things enables experts of given domains to create smart user experiences for interacting with the environment. However, development of such experiences requires strong programming skills, which are challenging to develop for non-technical users. This paper presents several extensions to the block-based programming language used in App Inventor to make the creation of mobile apps for smart learning experiences less challenging. Such apps are used to process and graphically represent data streams from sensors by applying map-reduce operations. A workshop with students without previous experience with Internet of Things (IoT) and mobile app programming was conducted to evaluate the propositions. As a result, students were able to create small IoT apps that ingest, process and visually represent data in a simpler form as using App Inventor's standard features. Besides, an experimental study was carried out in a mobile app development course with academics of diverse disciplines. Results showed it was faster and easier for novice programmers to develop the proposed app using new stream processing blocks.Spanish National Research Agency (AEI) - ERDF fund

Robot training using system identification

This paper focuses on developing a formal, theory-based design methodology to generate transparent robot control programs using mathematical functions. The research finds its theoretical roots in robot training and system identification techniques such as Armax (Auto-Regressive Moving Average models with eXogenous inputs) and Narmax (Non-linear Armax). These techniques produce linear and non-linear polynomial functions that model the relationship between a robot’s sensor perception and motor response. The main benefits of the proposed design methodology, compared to the traditional robot programming techniques are: (i) It is a fast and efficient way of generating robot control code, (ii) The generated robot control programs are transparent mathematical functions that can be used to form hypotheses and theoretical analyses of robot behaviour, and (iii) It requires very little explicit knowledge of robot programming where end-users/programmers who do not have any specialised robot programming skills can nevertheless generate task-achieving sensor-motor couplings. The nature of this research is concerned with obtaining sensor-motor couplings, be it through human demonstration via the robot, direct human demonstration, or other means. The viability of our methodology has been demonstrated by teaching various mobile robots different sensor-motor tasks such as wall following, corridor passing, door traversal and route learning