Design Of Programming Environments Research Articles

Interactive programming paradigm for real-time experimentation with remote living matter

Recent advancements in life-science instrumentation and automation enable entirely new modes of human interaction with microbiological processes and corresponding applications for science and education through biology cloud laboratories. A critical barrier for remote and on-site life-science experimentation (for both experts and nonexperts alike) is the absence of suitable abstractions and interfaces for programming living matter. To this end we conceptualize a programming paradigm that provides stimulus and sensor control functions for real-time manipulation of physical biological matter. Additionally, a simulation mode facilitates higher user throughput, program debugging, and biophysical modeling. To evaluate this paradigm, we implemented a JavaScript-based web toolkit, "Bioty," that supports real-time interaction with swarms of phototactic Euglena cells hosted on a cloud laboratory. Studies with remote and on-site users demonstrate that individuals with little to no biology knowledge and intermediate programming knowledge were able to successfully create and use scientific applications and games. This work informs the design of programming environments for controlling living matter in general, for living material microfabrication and swarm robotics applications, and for lowering the access barriers to the life sciences for professional and citizen scientists, learners, and the lay public.

Evaluating environments for functional programming

Functional programming presents new challenges in the design of programming environments. In a strongly typed functional language, such as ML, much conventional debugging of runtime errors is replaced by dealing with compile-time error reports. On the other hand, the cleanness of functional programming opens up new possibilities for incorporating sophisticated correctness-checking techniques into such environments. CYNTHIA is a novel editor for ML that both addresses the challenges and explores the possibilities. It uses an underlying proof system as a framework for automatically checking for semantic errors such as non-termination. In addition, CYNTHIA embodies the idea of programming by analogy—whereby users write programs by applying abstract transformations to existing programs. This paper investigatesCYNTHIA 's potential as a novice ML programming environment. We report on two studies in which it was found that students using CYNTHIA commit fewer errors and correct errors more quickly than when using a compiler/text editor approach.

Why Is Programming (Sometimes) So Difficult? Programming as Scientific Discovery in Multiple Problem Spaces

Our theoretical framework views programming as search in three problem spaces: rule, instance, and representation. The main objectives of this study are to find out how programmers change representation while working in multiple problem spaces, and how representation change increases the difficulty of programming tasks. Our theory of programming indicates that programming is similar to the way scientists discover and test theories. That is, programmers generate hypotheses in the rule space and test these hypotheses in the instance space. Moreover, programmers change their representations in the representation space when rule development becomes too difficult or alternative representations are available. We conducted three empirical studies with different programming tasks: writing a new program, understanding an existing program, and reusing an old program. Our results indicate that considerable cognitive difficulties stem from the need to change representations in these tasks. We conclude by discussing the implications of viewing programming as a scientific discovery for the design of programming environments and training methods.

Heterogeneous computing: challenges and opportunities

The issues and problems posed by heterogeneous computing are discussed. They include design of algorithms for applications, partitioning and mapping of application tasks, interconnection requirements, and the design of programming environments. The use of heterogeneous computing in image understanding is reviewed. An example vision task is presented, and the different types of parallelism used in the example are identified.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An integrated framework for the design of distributed programming environments

The design of programming environments implies the integration of different tools in order to provide the user with an uniform framework. Tools integration may be envisaged from different points of view: from a merely visual integration among non-cooperative tools, to a complete data and control integration. This paper is intended to present the work done on designing an architecture suitable to serve as a framework for distributed programming environments, together with a user interface generator able to provide a visual integration level to UNIX-like toolsets. This architecture is based on the use of a syntactic server able to manage partial attributed abstract syntax trees, and an environment controller in order to ensure the consistency among partial AST's.