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Matthew C. Davis

dblp:354/8660 · DBLP profile ↗
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2ranked-venue papers
2as first author
2since 2021 · last 2023
0000-0003-2366-8436ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 2 · 2 first-author · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
2 papers
Empirical software engineering · 50% Software testing · 25% Program analysis · 25%
Human-computer interaction and pervasive computing
2 papers
User interface design and tools · 50% Design research and methods · 50%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing › test generation
automated test generation
0.712023
NaNofuzz: A Usable Tool for Automatic Test Generation · ESEC/SIGSOFT FSE 2023
Program analysis › static analysis
bug detection
0.712023
NaNofuzz: A Usable Tool for Automatic Test Generation · ESEC/SIGSOFT FSE 2023
Empirical software engineering
developer studies
0.712023
What's (Not) Working in Programmer User Studies? · ACM Trans. Softw. Eng. Methodol. 2023
User interface design and tools
developer tools
0.212023
NaNofuzz: A Usable Tool for Automatic Test Generation · ESEC/SIGSOFT FSE 2023
Design research and methods › research methodology
study design
0.212023
What's (Not) Working in Programmer User Studies? · ACM Trans. Softw. Eng. Methodol. 2023

Methods — techniques the papers use, named apart from their topics

taxonomy · 1.3randomized controlled trial · 1.3interviews · 1.3focus groups · 1.3
YearPublicationVenuePosition
2023 NaNofuzz: A Usable Tool for Automatic Test Generation
abstract
In the United States alone, software testing labor is estimated to cost $48 billion USD per year. Despite widespread test execution automation and automation in other areas of software engineering, test suites continue to be created manually by software engineers. We have built a test generation tool, called NaNofuzz, that helps users find bugs in their code by suggesting tests where the output is likely indicative of a bug, e.g., that return NaN (not-a-number) values. NaNofuzz is an interactive tool embedded in a development environment to fit into the programmer's workflow. NaNofuzz tests a function with as little as one button press, analyses the program to determine inputs it should evaluate, executes the program on those inputs, and categorizes outputs to prioritize likely bugs. We conducted a randomized controlled trial with 28 professional software engineers using NaNofuzz as the intervention treatment and the popular manual testing tool, Jest, as the control treatment. Participants using NaNofuzz on average identified bugs more accurately (p < .05, by 30%), were more confident in their tests (p < .03, by 20%), and finished their tasks more quickly (p < .007, by 30%).
Matthew C. Davis, Sangheon Choi, Sam Estep, Brad A. Myers, Joshua Sunshine
ESEC/SIGSOFT FSE1
2023 What's (Not) Working in Programmer User Studies?
abstract
A key goal of software engineering research is to improve the environments, tools, languages, and techniques programmers use to efficiently create quality software. Successfully designing these tools and demonstrating their effectiveness involves engaging with tool users—software engineers. Researchers often want to conduct user studies of software engineers to collect direct evidence. However, running user studies can be difficult, and researchers may lack solution strategies to overcome the barriers, so they may avoid user studies. To understand the challenges researchers face when conducting programmer user studies, we interviewed 26 researchers. Based on the analysis of interview data, we contribute (i) a taxonomy of 18 barriers researchers encounter; (ii) 23 solution strategies some researchers use to address 8 of the 18 barriers in their own studies; and (iii) 4 design ideas, which we adapted from the behavioral science community, that may lower 8 additional barriers. To validate the design ideas, we held an in-person all-day focus group with 16 researchers.
Matthew C. Davis, Emad Aghayi, Thomas D. LaToza, Xiaoyin Wang, Brad A. Myers, Joshua Sunshine
ACM Trans. Softw. Eng. Methodol.1