Once a programmer knows one language, they can leverage concepts and knowledge already learned, and easily pick up another programming language. But is that always the case? To understand if programmers have difficulty learning additional programming languages, we conducted an empirical study of Stack Overflow questions across 18 different programming languages. We hypothesized that previous knowledge could potentially interfere with learning a new programming language. From our inspection of 450 Stack Overflow questions, we found 276 instances of interference that occurred due to faulty assumptions originating from knowledge about a different language. To understand why these difficulties occurred, we conducted semi-structured interviews with 16 professional programmers. The interviews revealed that programmers make failed attempts to relate a new programming language with what they already know. Our findings inform design implications for technical authors, toolsmiths, and language designers, such as designing documentation and automated tools that reduce interference, anticipating uncommon language transitions during language design, and welcoming programmers not just into a language, but its entire ecosystem.
Nischal Shrestha North Carolina State University
Colton Botta North Carolina State University
Tue 6 Oct
| 10:50 - 12:30: Paper Presentations - Cognition at TBD4 |
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| 10:50 - 11:10 Talk |
A Tale from the Trenches: Cognitive Biases and Software Development Technical Souti Chattopadhyay Graduate Student , Nicholas Nelson Oregon State University , Audrey Au Oregon State University , Natalia Morales Oregon State University , Christopher Sanchez Oregon State University , Rahul Pandita Phase Change Software , Anita Sarma Oregon State University |
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| 11:10 - 11:30 Talk |
Recognizing Developers' Emotions while Programming Technical Daniela Girardi University of Bari , Nicole Novielli University of Bari , Davide Fucci Blekinge Institute of Technology , Filippo Lanubile University of Bari |
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| 11:30 - 11:50 Talk |
Neurological Divide: An fMRI Study of Prose and Code Writing Technical Ryan Krueger University of Michigan , Yu Huang University of Michigan , Xinyu Liu University of Michigan , Tyler Santander University of California at Santa Barbara , Westley Weimer University of Michigan, USA , Kevin Leach University of Michigan |
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| 11:50 - 12:10 Talk |
Assessing Practitioner Beliefs SEIP Shrikanth N C North Carolina State University , Tim Menzies North Carolina State University |
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| 12:10 - 12:30 Talk |
Here We Go Again: Why Is It Difficult for Developers to Learn Another Programming Language? Technical Nischal Shrestha North Carolina State University , Colton Botta North Carolina State University , Titus Barik Microsoft , Chris Parnin North Carolina State University |
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机器学习及其应用2007
周志华 编 / 清华大学 / 2007-10 / 37.00元
机器学习是人工智能的一个核心研究领域，也是近年来计算机科学中最活跃的研究分支之一。目前，机器学习技术不仅在计算机科学的众多领域中大显身手，还成为一些交叉学科的重要支撑技术。本书邀请相关领域的专家撰文，以综述的形式介绍机器学习中一些领域的研究进展。全书共分13章，内容涉及高维数据降维、特征选择、支持向量机、聚类、强化学习、半监督学习、复杂网络、异构数据、商空间、距离度量以及机器学习在自然语言处理中的......一起来看看 《机器学习及其应用2007》 这本书的介绍吧!
