Measuring and modeling programming experience

Siegmund, Janet; Kästner, Christian; Liebig, Jörg; Apel, Sven; Hanenberg, Stefan

doi:10.1007/s10664-013-9286-4

Cited by 123 publications

(66 citation statements)

References 30 publications

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“…In the past, researchers investigated certain aspects of software development expertise (SDExp) such as the influence of programming experience [95], desired attributes of software engineers [63], or the time it takes for developers to become "fluent" in software projects [117]. However, there is currently no theory combining those individual aspects.…”

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confidence: 99%

Towards a theory of software development expertise

Baltes

Diehl

2018

Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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Software development includes diverse tasks such as implementing new features, analyzing requirements, and fixing bugs. Being an expert in those tasks requires a certain set of skills, knowledge, and experience. Several studies investigated individual aspects of software development expertise, but what is missing is a comprehensive theory. We present a first conceptual theory of software development expertise that is grounded in data from a mixed-methods survey with 335 software developers and in literature on expertise and expert performance. Our theory currently focuses on programming, but already provides valuable insights for researchers, developers, and employers. The theory describes important properties of software development expertise and which factors foster or hinder its formation, including how developers' performance may decline over time. Moreover, our quantitative results show that developers' expertise self-assessments are context-dependent and that experience is not necessarily related to expertise. CCS CONCEPTS• Software and its engineering;

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mentioning

confidence: 99%

Towards a theory of software development expertise

Baltes

Diehl

2018

Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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“…al. [19], [55] asked students a set of questions about their programming experience and then, by means of a controlled experiment, contrasted their answers with the performance of the respondents in program comprehension tasks. They report a strong correlation between the number of tasks successfully concluded and the self-estimates.…”

Section: Related Workmentioning

confidence: 99%

Identifying Experts in Software Libraries and Frameworks Among GitHub Users

Montandon

Silva

Valente

2019

2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)

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Software development increasingly depends on libraries and frameworks to increase productivity and reduce time-to-market. Despite this fact, we still lack techniques to assess developers expertise in widely popular libraries and frameworks. In this paper, we evaluate the performance of unsupervised (based on clustering) and supervised machine learning classifiers (Random Forest and SVM) to identify experts in three popular JavaScript libraries: facebook/react, mongodb/node-mongodb, and socketio/socket.io. First, we collect 13 features about developers activity on GitHub projects, including commits on source code files that depend on these libraries. We also build a ground truth including the expertise of 575 developers on the studied libraries, as self-reported by them in a survey. Based on our findings, we document the challenges of using machine learning classifiers to predict expertise in software libraries, using features extracted from GitHub. Then, we propose a method to identify library experts based on clustering feature data from GitHub; by triangulating the results of this method with information available on Linkedin profiles, we show that it is able to recommend dozens of GitHub users with evidences of being experts in the studied JavaScript libraries. We also provide a public dataset with the expertise of 575 developers on the studied libraries.

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“…A recent survey [14] showed both a lack of consistent measures of expertise in extant program comprehension studies, as well as the shortfalls of self-assessment data in predicting expertise.…”

Section: Program Comprehension and Experiencementioning

confidence: 99%

“…Each participant completed an expertise survey following the completion of the experimental session, similar to the one used in Siegmund et al [14].…”

Section: A Proceduresmentioning

confidence: 99%

Toward using alpha and theta brain waves to quantify programmer expertise

Crk

Kluthe

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Empirical studies of programming language learnability and usability have thus far depended on indirect measures of human cognitive performance, attempting to capture what is at its essence a purely cognitive exercise through various indicators of comprehension, such as the correctness of coding tasks or the time spent working out the meaning of code and producing acceptable solutions. Understanding program comprehension is essential to understanding the inherent complexity of programming languages, and ultimately, having a measure of mental effort based on direct observation of the brain at work will illuminate the nature of the work of programming. We provide evidence of direct observation of the cognitive effort associated with programming tasks, through a carefully constructed empirical study using a cross-section of undergraduate computer science students and an inexpensive, off-the-shelf brain-computer interface device. This study presents a link between expertise and programming language comprehension, draws conclusions about the observed indicators of cognitive effort using recent cognitive theories, and proposes directions for future work that is now possible.

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Measuring and modeling programming experience

Cited by 123 publications

References 30 publications

Towards a theory of software development expertise

Towards a theory of software development expertise

Identifying Experts in Software Libraries and Frameworks Among GitHub Users

Toward using alpha and theta brain waves to quantify programmer expertise

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