Studying programmer behavior experimentally: the problems of proper methodology
Communications of the ACM
Some basic determinants of computer programming productivity
Communications of the ACM
Experimental investigations of the utility of detailed flowcharts in programming
Communications of the ACM
Grade and ability predictions in an introductory programming course
ACM SIGCSE Bulletin
Using a behavioral theory of program comprehension in software engineering
ICSE '78 Proceedings of the 3rd international conference on Software engineering
An experimental investigation of the effect of program structure on program understanding
Proceedings of an ACM conference on Language design for reliable software
Psychological complexity of computer programs: an experimental methodology
ACM SIGPLAN Notices
Software development snapshots: A preliminary investigation
ACM SIGCHI Bulletin
A comparison of Lisp, Prolog, and Ada programming productivity in AI area
ICSE '85 Proceedings of the 8th international conference on Software engineering
Problems with software complexity measurement
CSC '85 Proceedings of the 1985 ACM thirteenth annual conference on Computer Science
A review of human factors research on programming languages and specifications
CHI '82 Proceedings of the 1982 Conference on Human Factors in Computing Systems
Design of command menus for CAD systems
DAC '82 Proceedings of the 19th Design Automation Conference
A theory of small program complexity
ACM SIGPLAN Notices
On the complexity of measuring software complexity
AFIPS '81 Proceedings of the May 4-7, 1981, national computer conference
Guidelines for conducting and reporting case study research in software engineering
Empirical Software Engineering
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Within the past decade Computer Science researchers have begun to use controlled experimentation to address questions of human factors in the programming process. However, some of this work has been criticized for lack of experimental controls, insufficient sample sizes, and questionable generality. In a study of 160 student and professional programmers, several key biographical factors have been identified which account for a large proportion of performance variation on typical experimental tasks. The study has implications for subject selection and interpretation of results in software experimentation. Our findings should lead to improved experimental design and analysis techniques with increased confidence in the results of software psychology studies.