자료유형  

 학위논문

Control Number  

0017164573

International Standard Book Number  

9798384045991

Dewey Decimal Classification Number  

004

Main Entry-Personal Name  

Endres, Madeline.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Michigan., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

217 p.

General Note  

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

General Note  

Advisor: Weimer, Westley R.

Dissertation Note  

Thesis (Ph.D.)--University of Michigan, 2024.

Summary, Etc.  

요약In this dissertation, we present a series of algorithms and theoretically-grounded interventions that enhance programmer productivity. By combining large-scale exploratory empirical investigations with controlled human-focused experimental design, we both build mathematical models of the impact of understudied features on programmer productivity and also provide actionable, evidence-backed interventions that improve productivity in practice for targeted diverse programmer groups. We present findings from three primary lenses: developing efficient and usable bug-fixing tools for non-traditional novices, designing effective programming training informed by objective measures of programming cognition, and understanding the impact of external factors, such as psychoactive substance use. We briefly discuss the work conducted in each lens:1. Developing Efficient and Usable Programming Tools: We propose and evaluate two novel methods of bug-fixing support targeting parse-errors and input-related bugs. Both are error types that we identify as commonly-encountered by non-traditional novice programmers (e.g., those learning without the support of the traditional classroom) but are overlooked by existing program-repair tools. 2. Designing Effective Developer Training: To help novice programmers become more like experts faster, we develop a model of novice programming expertise using neuroimaging. We leverage our cognitive findings to design and evaluate a novel supplemental reading training that improves programming outcomes.3. Understanding External Productivity Barriers: We argue that external factors also impact software productivity, including those anecdotally-reported but understudied by the scientific literature. In this dissertation, we study the impact of one such factor: psychoactive substance use. We both conduct the first survey of the prevalence of such substances in software and also develop a mathematical model of the true impact of one such substance, cannabis, on programming ability.In this dissertation, we not only argue that varied external support can improve developer productivity, but we also specify which support can best do so. We contend that understudied factors and potential interventions can be identified through large-scale exploratory analyses. In addition, we show how the impact of targeted interventions can be measured via causal experimental designs and large-scale human evaluations, even for factors impacting diverse populations that have previously only been considered anecdotally. 

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Neurosciences.

Subject Added Entry-Topical Term  

Bioinformatics.

Subject Added Entry-Topical Term  

Medical imaging.

Subject Added Entry-Topical Term  

Cognitive psychology.

Index Term-Uncontrolled  

Software development productivity

Index Term-Uncontrolled  

Novice programming expertise

Index Term-Uncontrolled  

Software productivity

Index Term-Uncontrolled  

Psychoactive substance use

Index Term-Uncontrolled  

Neuroimaging

Added Entry-Corporate Name  

University of Michigan Computer Science & Engineering

Host Item Entry  

Dissertations Abstracts International. 86-03B.

Electronic Location and Access  

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Control Number  

joongbu:654931