Subtopic Deep Dive
End-User Software Engineering
Research Guide
What is End-User Software Engineering?
End-User Software Engineering (EUSE) provides programming support, tools, and practices for non-professional developers creating software through spreadsheets, macros, visual languages, and domain-specific environments.
EUSE research examines debugging behaviors, tool designs, and evolution patterns in end-user computing. Key works include systematic mapping by Barricelli et al. (2018, 249 citations) and foundational texts by Nardi (1993, 421 citations) and Rockart & Flannery (1983, 499 citations). Over 10 major papers span from 1983 to 2023.
Why It Matters
EUSE enables knowledge workers to build robust applications without formal training, reducing reliance on professional developers (Nardi, 1993). It supports spreadsheet formula debugging and macro evolution in business settings (Rockart & Flannery, 1983). Tools like Variolite aid data scientists in iterative scripting (Kery et al., 2017). Modern extensions apply to LLM prompt engineering by non-experts (Zamfirescu-Pereira et al., 2023).
Key Research Challenges
Debugging End-User Artifacts
End users struggle with errors in spreadsheets and macros due to limited mental models of computation. Nardi (1993) documents barriers in commanding programming power. Tools must bridge this without overwhelming novices (Fischer & Girgensohn, 1990).
Tool Design for Non-Experts
Creating accessible interfaces for creative extensions remains difficult. Resnick et al. (2005) outline principles for supporting creative thinking in tools. End-user modifiability requires balancing flexibility and simplicity (Fischer & Girgensohn, 1990).
Managing Software Evolution
End-user programs evolve unpredictably, complicating maintenance. Barricelli et al. (2018) map studies showing gaps in evolution support. Rockart & Flannery (1983) classify user types needing differentiated controls.
Essential Papers
Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts
J.D. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann et al. · 2023 · 661 citations
Pre-trained large language models ("LLMs") like GPT-3 can engage in fluent, multi-turn instruction-taking out-of-the-box, making them attractive materials for designing natural language interaction...
A small matter of programming: perspectives on end user computing
· 1994 · Choice Reviews Online · 606 citations
From the Publisher: A Small Matter of Programming asks why it has been so difficult for end users to command programming power and explores the problems of end-user-driven application development ...
The management of end user computing
John F. Rockart, Lauren S. Flannery · 1983 · Communications of the ACM · 499 citations
End users can be classified into six distinct types. Each of them needs differentiated education, support, and control from the Information Systems function. To support a large number of their appl...
A Small Matter of Programming
Bonnie Nardi · 1993 · The MIT Press eBooks · 421 citations
A Small Matter of Programming asks why it has been so difficult for end users to command programming power and explores the problems of end user-driven application development that must be solved t...
End-user development, end-user programming and end-user software engineering: A systematic mapping study
Barbara Rita Barricelli, Fabio Cassano, Daniela Fogli et al. · 2018 · Journal of Systems and Software · 249 citations
Design Principles for Tools to Support Creative Thinking
Mitchel Resnick, Brad A. Myers, Kumiyo Nakakoji et al. · 2005 · OPAL (Open@LaTrobe) (La Trobe University) · 201 citations
Institute for Software Research
Empowering End Users to Customize their Smart Environments
Giuseppe Desolda, Carmelo Ardito, Maristella Matera · 2017 · ACM Transactions on Computer-Human Interaction · 201 citations
Research on the Internet of Things (IoT) has devoted many efforts to technological aspects. Little social and practical benefits have emerged so far. IoT devices, so-called smart objects , are beco...
Reading Guide
Foundational Papers
Start with Nardi (1993) for core problems in end-user programming power; Rockart & Flannery (1983) for user classifications and management; Fischer & Girgensohn (1990) for modifiability concepts.
Recent Advances
Study Barricelli et al. (2018) systematic mapping; Kery et al. (2017) Variolite for data scripting; Zamfirescu-Pereira et al. (2023) for LLM prompt failures.
Core Methods
Core techniques: ethnographic studies (Nardi, 1993), design principles (Resnick et al., 2005), systematic literature mapping (Barricelli et al., 2018), ideation tools like Variolite (Kery et al., 2017).
How PapersFlow Helps You Research End-User Software Engineering
Discover & Search
Research Agent uses searchPapers and citationGraph to map EUSE literature from Nardi (1993), revealing clusters around debugging and tools. exaSearch finds niche works like Variolite (Kery et al., 2017); findSimilarPapers expands from Barricelli et al. (2018) systematic mapping.
Analyze & Verify
Analysis Agent applies readPaperContent to extract debugging practices from Nardi (1993), then verifyResponse with CoVe checks claims against Rockart & Flannery (1983). runPythonAnalysis simulates spreadsheet error patterns with pandas; GRADE scores evidence strength for tool design principles (Resnick et al., 2005).
Synthesize & Write
Synthesis Agent detects gaps in end-user modifiability support post-Fischer & Girgensohn (1990), flagging contradictions in user classifications (Rockart & Flannery, 1983). Writing Agent uses latexEditText, latexSyncCitations for EUSE surveys, latexCompile for reports, and exportMermaid for tool evolution diagrams.
Use Cases
"Analyze spreadsheet debugging patterns from EUSE papers using code examples."
Research Agent → searchPapers('end-user spreadsheet debugging') → Analysis Agent → runPythonAnalysis(pandas simulation of formula errors from Kery et al. 2017) → researcher gets error rate stats and visualizations.
"Write a LaTeX survey on EUSE tool principles citing Nardi and Resnick."
Synthesis Agent → gap detection on tool designs → Writing Agent → latexEditText + latexSyncCitations(Nardi 1993, Resnick 2005) → latexCompile → researcher gets compiled PDF with synced bibliography.
"Find GitHub repos for Variolite and similar EUSE tools."
Research Agent → citationGraph(Variolite Kery 2017) → Code Discovery workflow (paperExtractUrls → paperFindGithubRepo → githubRepoInspect) → researcher gets repo code, usage examples, and fork analysis.
Automated Workflows
Deep Research workflow conducts systematic reviews of 50+ EUSE papers: searchPapers → citationGraph → DeepScan (7-step analysis with GRADE checkpoints on Nardi 1993 claims). Theorizer generates theories on prompt debugging from Zamfirescu-Pereira et al. (2023) via literature synthesis. DeepScan verifies tool principles (Resnick 2005) with CoVe chain-of-verification.
Frequently Asked Questions
What defines End-User Software Engineering?
EUSE supports non-professionals in programming via spreadsheets, macros, and visual tools, focusing on debugging and evolution (Barricelli et al., 2018).
What are key methods in EUSE?
Methods include user studies of programming barriers (Nardi, 1993), systematic mappings (Barricelli et al., 2018), and design principles for creative tools (Resnick et al., 2005).
What are major EUSE papers?
Foundational: Nardi (1993, 421 citations), Rockart & Flannery (1983, 499 citations). Recent: Barricelli et al. (2018, 249 citations), Kery et al. (2017, 131 citations).
What open problems exist in EUSE?
Challenges include scalable debugging for spreadsheets, evolution support for macros, and tools for LLM prompting by non-experts (Zamfirescu-Pereira et al., 2023).
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