As given at Devworld 2024

1. How good of a
developer are you?
And can we objectively measure this?

2. Bio
• Roy Wasse
• Bsc. Technical
Computer Science
• Msc. Philosophy of
Technology
• Co Founder OpenValue
• Dev & Management roles
• 15+ years of hiring
experience

3. Is someone is a
‘good’ dev!
• Do you know YAGNI or HATEOS, SOLID?
• Explain balanced binary tree search algo?
• Use 20 best interview questions from
internet
• Apply Open Hiring?
• Proxy variables e.g., CV, experience,
education, references, questionnaire
• Whiteboard coding
• Make (lengthy) assignment

4. Google Hiring process
• Assessments: You may be asked to do a brief online assessment, like a coding quiz, after
you’ve submitted your resume.
• Short virtual chats: Before diving into more in-depth interviews, you’ll typically have
one or two shorter conversations over phone or video. These will usually be with a
recruiter and then with either the hiring manager or peer on the team, and are designed
to assess key skills you’ll need for the role.
• Project work: We sometimes ask candidates to complete a small project prior to their in-
depth interviews. This could range from prepping a case study to providing writing or
code samples (don’t stress, they’re not that scary and we won’t spring this on you
without warning), and helps us understand how you think and approach problems. We’ll let
you know about any additional materials we’ll need early on.
• Interviews: We get excited about interviewing and take it seriously because, at the risk
of sounding cliché, Google is what Googlers make it. Our process can be rigorous
(typically 3-4 interviews in one day, either over video or in person).

5. • Can science help us and
how?
• Let’s dive into
literature
• And see how we can
predict of someone will
be a good dev!
Who knows what
works?!!

6. Use of Aptitude tests
• Measure if someone is dispositioned well to excel
• Popular in the 1970s (e.g., Alspaugh, 1972; Mazlack,
1980)
• due to lack of good results from these tests not used
much anymore (Bornat, Dehnadi, & Simon, 2008; Curtis,
1991; Mayer & Stalnaker, 1968)
• Also: in a sample of over 3,500 students, no
incremental validity was found for specialized aptitude
tests for programmers over the more general aptitude
tests used by the United States Air Force (Besetsny,
Ree, & Earles, 1993)

7. Role of
education
• Large meta study (Roth,
BeVier, Switzer, &
Schippmann, 1996)
reported a correlation of
r = 0.16
• Shortly after graduation,
correlation between
grades and job
performance much higher
• 1 year, r = 0.23,
n = 1,288
• 6 years, r = 0.05,
n = 866.

8. Role of
experience
• Studies show positive effect
of having programming
experience (Agarwal, Sinha &
Tanniru, 1996; Arisholm &
Sjøberg, 2004)
• Other studies have reported
no effect of experience
beyond the first few years
(Jeffery & Lawrence, 1979) or
no effect of experience
(Jørgensen, 1995; Wohlin,
2002, 2004)

9. Role of
Programming
knowledge
• Central predictor of skill is
knowledge (Chi et al., 1988)
• Used in Java certification
• But…software development is a
skill
Acquired by practice

10. What is skill?
• Ability (Ferguson, 1956):
“An ability is a trait
defined by what an
individual can do”
• “Skill is specialized type
of ability, one that
improves with practice and
is well adjusted, well
organized and goal
oriented” (Fitts & Posner,
1967)

11. Model of skill acquisition
• “A Five-Stage Model of the Mental Activities
Involved in Directed Skill Acquisition”,
(Dreyfus & Dreyfus, 1980)
• Flow is at level 5
• Also think of driving / playing chess

12. It’s all about skill?
• Skill cannot by equated with performance
• “the individual performed well because of his
high level of skill” is a generalization
(Messick, 1994)
• 8 major components of job performance research
show 3 direct determinants of performance on
the job: knowledge, skill and motivation
(Campbell, McCloy, Oppler & Sager, 1993)

13. Skill is just one..?
Campbell’s work performance framework proposed eight work performance
dimensions:
1. job-specific task proficiency <= skill
2. non–job-specific task proficiency
3. written and oral communications
4. demonstrating effort
5. maintaining personal discipline
6. facilitating peer and team performance
7. supervision
8. management and administration.
According to Campbell, these eight dimensions are sufficient to describe
the latent structure of performance at a general level. However, he also
noted that the eight factors can have different patterns of subdimensions,
and their content and salience can vary across jobs.

14. Technical skill is the most important one
(McGill, 2008)

15. Skill & project success
emphasizing low price when selecting software providers in bidding
rounds increases the likelihood project will fail.” (Jørgensen 2011)
Tested by analyzing a dataset of 4,791,067 bids for 785,326 small-
scale projects
Clients emphasizing a low price selected providers with lower skill,
resulted in 9% increase in failure rate

16. What about
teamwork
• Team performance is more
complex to understand
than individual
performance (see, e.g.,
Baker & Salas, 1992;
Volmer, 2006),
individual skill or
expertise is
nevertheless a central
component in team
performance (Land, Wong,
& Jeffery, 2003).

17. Deep dive
into skills
• And…how to objectively
measure coding skills
• Soft skills

19. Research overview
• Relations between effort estimates, skill indicators, and measured
programming skill. Magne Jørgensen, Gunnar R. Bergersen and Knut
Liestøl (2021)
• Measuring Programming Skill: Construction and Validation of an
Instrument for Evaluating Java Developers. Gunnar R. Bergersen,
PhD thesis (2015)
• Construction and validation of an instrument for measuring
programming skill. Gunnar R. Bergersen, Dag I. K. Sjøberg, and
Tore Dybå (2014)
• Evaluating methods and technologies in software engineering with
respect to developers’ skill level. Gunnar R. Bergersen and Dag I.
K. Sjøberg (2012)
• Inferring skill from tests of programming performance: combining
time and quality. Gunnar R. Bergersen, Jo E. Hannay, Dag I. K.
Sjøberg, Tore Dybå, and Amela Karahasanovic (2011)
• Programming skill, knowledge, and working memory among
professional developers from an investment theory perspective.
Gunnar R. Bergersen and Jan-Eric Gustafsson (2011)

20. How to measure skill?
• Home work assignment (better rotate those)
• Whiteboard coding (under pressure)
• Online questionaires (assuming correlation
between real world coding)
• All are subjective
• And probably you want to get it right!

21. Measure via self
assessment
• Ratings of self may work
better when people compare
themselves relative to
each other (people they
know) rather than when
providing absolute ratings
(Kruger and Dunning (1999)
Source:
wikipedia

22. Task performance is key
• >20 studies about job performance assessed
• Almost all frameworks mentioned task
performance as an important dimension of
individual work performance.

23. Predict skill
• Study of novice LISP
programmers (Anderson &
Jeffries, 1985) found that
“the best predictor of
individual subject
differences in errors on
problems that involved one
LISP concept was the number
of errors on other problems
that involved different
concepts” (Anderson, 1987,
p. 203).

24. How to put this into a system
• Time (Bergersen, 2011)
• Quality (Ebert et. al. 2005)
Create a measurement model & test the model on
new data
(Bergersen et al., 2014)

25. Rasch model
• More than 50 years ago, Rasch (1960) used tests
to study the reading performance of students.
• Rasch models have become a practical way to
measure psychological abilities

26. Task construction
1.Picked existing tasks from literature
2.Reanalyzed data sets to combine time & quality
3.Large control group with 255 developers from 9
countries used to score tasks

27. • Collection of tasks
• VM in the cloud (Linux + IntelliJ / Eclipse
• Interchangeable
Building a new standard to measure & analyze
software engineering skills_

28. Detailed analytics of skills and
meaningful comparisons with empirically validated norm groups_

29. Foojay community
• Over 1000 members
• 2 news articles every week day
• Podcast
• Conference information
• Tech questions
• And the certification initiative!

30. Conclusion
• Be careful using proxy variables and your gut
feeling for selecting devs
• Coding is mostly about skills
• Research shows how to measure skills
• So let’s do this!