arXiv subjects

Thomas J. Misa

Publications and source records attributed to Thomas J. Misa.

7 recordsLinked to original sources

arXiv2025

Dynamics of Gender Bias in Software Engineering

The field of software engineering is embedded in both engineering and computer science, and may embody gender biases endemic to both. This paper surveys software engineering's origins and its long-running attention to engineering professionalism, profiling five leaders; it then examines the field's recent attention to gender issues and gender bias. It next quantitatively analyzes women's participation as research authors in the field's leading International Conference of Software Engineering (1976-2010), finding a dozen years with statistically significant gender exclusion. Policy dimensions of research on gender bias in computing are suggested.

cs.SE↗

arXiv2024

Women's Participation in Computing: Evolving Research Methods

A 2022 keynote for the ACM History Committee on "Why SIG History Matters: New Data on Gender Bias in ACM's Founding SIGs 1970-2000" presented new data describing women's participation as research-article authors in 13 early ACM Special Interest Groups, finding significant growth in women's participation across 1970-2000 and, additionally, remarkable differences in women's participation between the SIGs. That presentation built on several earlier publications that developed a research method for assessing the number of women computer scientists that [a] are chronologically prior to the availability of the Bureau of Labor Statistics (BLS) data on women in the IT workforce; and [b] permit focused investigation of varied sub-fields within computing. This present report expands on these earlier articles, and their evolving research method, connecting them to the ACM SIG Heritage presentation. It also outlines some of the choices and considerations made in developing and refining "mixed methods" research (using both quantitative and qualitative approaches) as well as extensions of the research being currently explored.

cs.CY↗

arXiv2024

Dynamics of Gender Bias within Computer Science

A new dataset (N = 7,456) analyzes women's research authorship in the Association for Computing Machinery's founding 13 Special Interest Groups or SIGs, a proxy for computer science. ACM SIGs expanded during 1970-2000; each experienced increasing women's authorship. But diversity abounds. Several SIGs had fewer than 10% women authors while SIGUCCS (university computing centers) exceeded 40%. Three SIGs experienced accelerating growth in women's authorship; most, including a composite ACM, had decelerating growth. This research may encourage reform efforts, often focusing on general education or workforce factors (across the entity of "computer science"), to examine under-studied dynamics within computer science that shaped changes in women's participation.

cs.CY↗

arXiv2023

Charles Babbage, Ada Lovelace, and the Bernoulli Numbers

This chapter makes needed corrections to an unduly negative scholarly view of Ada Lovelace. Credit between Lovelace and Babbage is not a zero-sum game, where any credit added to Lovelace somehow detracts from Babbage. Ample evidence indicates Babbage and Lovelace each had important contributions to the famous 1843 Sketch of Babbage's Analytical Engine and the accompanying Notes. Further, Lovelace's correspondence with two highly accomplished figures in 19th century mathematics, Charles Babbage and Augustus De Morgan, establish her mathematical background and sophistication. Babbage and Lovelace's treatment of the Bernoulli numbers in Note 'G' spotlights this aspect of their collaboration. Finally, while acknowledging significant definitional problems in calling Lovelace the world's "first computer programmer," I affirm that Lovelace created an elemental sequence of instructions -- that is, an algorithm -- for computing the series of Bernoulli numbers.

math.HO↗

arXiv2022

Gender Bias in Big Data Analysis

This article combines humanistic "data critique" with informed inspection of big data analysis. It measures gender bias when gender prediction software tools (Gender API, Namsor, and Genderize.io) are used in historical big data research. Gender bias is measured by contrasting personally identified computer science authors in the well-regarded DBLP dataset (1950-1980) with exactly comparable results from the software tools. Implications for public understanding of gender bias in computing and the nature of the computing profession are outlined. Preliminary assessment of the Semantic Scholar dataset is presented. The conclusion combines humanistic approaches with selective use of big data methods.

cs.CY↗

arXiv2022

Gender Bias in Computing

This paper examines the historical dimension of gender bias in the US computing workforce. It offers new quantitative data on the computing workforce prior to the availability of US Census data in the 1970s. Computer user groups (including SHARE, Inc., and the Mark IV software user group) are taken as a cross-section of the computing workforce. A novel method of gender analysis is developed to estimate women's and men's participation in computing beginning in the 1950s. The data presented here are consistent with well-known NSF statistics that show computer science undergraduate programs enrolling increasing numbers of women students during 1965-1985. These findings challenge the 'making programming masculine' thesis, and serve to correct the unrealistically high figures often cited for women's participation in early computer programming. Gender bias in computing today is traced not to 1960s professionalization but to cultural changes in the 1980s and beyond.

cs.CY↗

arXiv2022

Temporal Analysis and Gender Bias in Computing

Recent studies of gender bias in computing use large datasets involving automatic predictions of gender to analyze computing publications, conferences, and other key populations. Gender bias is partly defined by software-driven algorithmic analysis, but widely used gender prediction tools can result in unacknowledged gender bias when used for historical research. Many names change ascribed gender over decades: the "Leslie problem." Systematic analysis of the Social Security Administration dataset -- each year, all given names, identified by ascribed gender and frequency of use -- in 1900, 1925, 1950, 1975, and 2000 permits a rigorous assessment of the "Leslie problem." This article identifies 300 given names with measurable "gender shifts" across 1925-1975, spotlighting the 50 given names with the largest such shifts. This article demonstrates, quantitatively, there is net "female shift" that likely results in the overcounting of women (and undercounting of men) in earlier decades, just as computer science was professionalizing. Some aspects of the widely accepted 'making programming masculine' perspective may need revision.

cs.CY↗