APL learning bugs

Eisenberg, Murray; Peelle, Howard A.

doi:10.1145/390005.801189

Cited by 2 publications

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A framework and methodology for studying the causes of software errors in programming systems

Myers

2005

Journal of Visual Languages & Computing

152

106

View full text Add to dashboard Cite

An essential aspect of programmers' work is the correctness of their code. This makes current HCI techniques ill-suited to analyze and design the programming systems that programmers use everyday, since these techniques focus more on problems with learnability and efficiency of use, and less on error-proneness. We propose a framework and methodology that focuses specifically on errors by supporting the description and identification of the causes of software errors in terms of chains of cognitive breakdowns. The framework is based on both old and new studies of programming, as well as general research on the mechanisms of human error. Our experiences using the framework and methodology to study the Alice programming system have directly inspired the design of several new programming tools and interfaces. This includes the Whyline debugging interface, which we have shown to reduce debugging time by a factor of 8 and help programmers get 40% further through their tasks. We discuss the framework's and methodology's implications for programming system design, software engineering, and the psychology of programming. r

show abstract

A framework and methodology for studying the causes of software errors in programming systems

Myers

2005

Journal of Visual Languages & Computing

152

106

View full text Add to dashboard Cite

show abstract

Better Together? An Evaluation of AI-Supported Code Translation

Weisz¹,

Müller²,

Ross³

et al. 2022

27th International Conference on Intelligent User Interfaces

View full text Add to dashboard Cite

Generative machine learning models have recently been applied to source code, for use cases including translating code between programming languages, creating documentation from code, and auto-completing methods. Yet, state-of-the-art models often produce code that is erroneous or incomplete. In a controlled study with 32 software engineers, we examined whether such imperfect outputs are helpful in the context of Java-to-Python code translation. When aided by the outputs of a code translation model, participants produced code with fewer errors than when working alone. We also examined how the quality and quantity of AI translations affected the work process and quality of outcomes, and observed that providing multiple translations had a larger impact on the translation process than varying the quality of provided translations. Our results tell a complex, nuanced story about the benefits of generative code models and the challenges software engineers face when working with their outputs. Our work motivates the need for intelligent user interfaces that help software engineers effectively work with generative code models in order to understand and evaluate their outputs and achieve superior outcomes to working alone.CCS Concepts: • Human-centered computing → HCI theory, concepts and models; • Software and its engineering → Designing software; • Computing methodologies → Generative and developmental approaches.

show abstract

APL learning bugs

Cited by 2 publications

References 5 publications

A framework and methodology for studying the causes of software errors in programming systems

A framework and methodology for studying the causes of software errors in programming systems

Better Together? An Evaluation of AI-Supported Code Translation

Contact Info

Product

Resources

About