Hiring is Broken: What Do Developers Say
About Technical Interviews?
North Carolina State University
Raleigh, NC, USA
North Carolina State University
Raleigh, NC, USA
Redmond, WA, USA
Abstract—Technical interviews—a problem-solving form of
interview in which candidates write code—are commonplace
in the software industry, and are used by several well-known
companies including Facebook, Google, and Microsoft. These
interviews are intended to objectively assess candidates and
determine ﬁt within the company. But what do developers say
To understand developer perceptions about technical inter-
views, we conducted a qualitative study using the online social
news website, Hacker News—a venue for software practitioners.
Hacker News posters report several concerns and negative
perceptions about interviews, including their lack of real-world
relevance, bias towards younger developers, and demanding
time commitment. Posters report that these interviews cause
unnecessary anxiety and frustration, requiring them to learn
arbitrary, implicit, and obscure norms. The ﬁndings from our
study inform inclusive hiring guidelines for technical interviews,
such as collaborative problem-solving sessions.
Index Terms—diversity and inclusion, Hacker News, program-
ming, software engineering, technical interviews, whiteboard
PRE LU DE
Let’s begin with a technical interview problem. Consider the
following coding question from LeetCode,1an online platform
for preparing software development candidates for interviews:
Before going further—and regardless of your coding
proﬁciency—we’d like you to spend a few minutes and take
a stab at this question.
1You can solve the problem interactively at https://leetcode.com/problems/
maximum-subarray/. The provably optimal solution to this question—called
Kadane’s algorithm—is described in Bentley’s 1984 column, Programming
Pearls . The column presents various solutions to this question with cubic,
quadratic, and linear time complexities. Bentley notes that the problem “is
really a ‘toy’—it was never incorporated into a system.”
Well, how did it go? Did you ﬁnd an O(n) solution?
Developers within the LeetCode community report that—
within the past six months—this coding question has been used
in technical interviews at well-known software companies such
as Apple, Amazon, Microsoft, Google, Facebook, and Uber.
A technical interview for software development consists
of one or more stages within the interview life cycle ,
. It begins with an initial screening of the candidate,
usually conducted over the phone or through an online coding
site, such as CoderPad.2Depending on their performance, the
candidate may be invited for an on-site visit. This on-site
visit consists of a series of one-on-one interviews (each 45
minutes to an hour, over a period of half a day to several
days) with engineers, and sometimes managers. The interviews
primarily focus on technical coding or algorithms, either on
the whiteboard or using a simple text editor on a computer.
In other words, technical interviews are primarily a test of the
candidates’ problem-solving or “analytical ability” . And
if all goes well, the candidate can expect to receive an offer.
For hiring managers, technical interview have considerable
appeal . First, hiring managers are able to ask questions
directly from the companies’ question bank, instead of having
to design their own interview questions. Second, the format
purports to reduce variation between interviewers and teams,
since candidates can be evaluated through objective scoring
criteria. Finally, the interview process becomes scalable: new
interviewers are straight-forward to train, and these interview-
ers can interchangeably ask coding questions to any software
engineering candidate. All of these, in theory, result in a
more-or-less standardized and meritocratic technical interview
The collective experiences of interview candidates, however,
appear to tell a very different story—even a cursory glance
reveals that “technical interviews are broken” , that they
are an “antagonistic”  form of high-pressure “whiteboard
algorithm hazing”  that have “nothing to do with real day-
to-day developer work” , are “humiliat[ing] profession-
ally” , assess candidates through an “algorithm question
lottery [of] luck and chance” , and require substantial
“upfront investment” from the candidate  to learn the
“cultural norms necessary to get themselves into a desk at
a technology ﬁrm” . Still others argue that technical
interviews may even “promote exclusion and discrimination,
serving only as a barrier to entry for qualiﬁed underrepresented
The goal of this paper is to take meaningful, personal, and
yet disjoint anecdotes such as these—and amplify them into a
principled theoretical foundation to support research towards
improving technical interviews in software development. To
that end, we conducted a qualitative study in which we
obtained over forty six thousand authored comments from
Hacker News—a social website for software practitioners
focusing on computer science, software development, and
entrepreneurship—pertaining to the topic “interviews.” We
framed these comments through the analytical lens of small
stories —stories of their personal experiences and their past
events—and through thematic analysis  identiﬁed concerns
software developers have about technical interviews. The
contribution of this paper is a state-of-the-practice synthesis of
concerns from the Hacker News community about technical
interviews for software developers, reﬂected through their own
Our analysis of Hacker News identiﬁes several concerns in
current software engineering practices with regards to techni-
cal interviews. Though hiring managers justify these practices
as being meritocratic, our ﬁndings suggest that candidates
perceive these practices as subjective, arbitrary, unnecessarily
stressful, non-inclusive—and at times—demeaning to their
sense of self-worth and self-efﬁcacy. We propose guidelines to
make hiring more inclusive and equitable without sacriﬁcing
interviewing effectiveness, for example, providing candidates
with explicit evaluation criteria in advance.
Research context. We used Hacker News, a social website
for software practitioners, to conduct our investigation. As
a community, Hacker News contains over 1.5 million user-
submitted comments on a variety of cultural and technical
topics of signiﬁcance to the hacker community (for example,
“F.C.C. Repeals Net Neutrality Rules,” “CIA malware and
hacking tools,” and “How to Pass a Programming Interview,”
to convey a sense of the diversity of topics). Wu and col-
leagues, through a survey with software developers who use
GitHub, found that Hacker News serves as an important venue
for software developers to exchange ideas as part of a broader
cultural ecosystem . Barik and colleagues conducted a
formative study using Hacker News to demonstrate that in-
vestigations within the online community can yield insights
into qualitative research topics, for example to understand how
software developers interpret programming and play , .
We adopt this approach to the investigation of concerns with
Data collection. We used the Algolia3search engine API,
EXT REM E SENTIMENTS BY TIT LE
Title Points Comments Polarity1
Most Negative Titles
Programmers are confessing their
sins to protest a broken job inter-
303 226 -0.80
UBER ATC is disguising research
as fake job interviews?
133 31 -0.75
Ask HN: Failed interview, feel-
ing unemployable and depressed—
what do I do?
377 234 -0.73
My Day Interviewing for the Ser-
vice Economy Startup from Hell
604 274 -0.68
The Programming Interview from
174 136 -0.68
Most Positive Titles
Best interview questions to spot
41 22 0.82
Thanks HN: Developers and YC
companies video speed interview
38 12 0.74
Ask HN: What should an ideal
developer interview process look
261 278 0.70
Ask HN: What are good tech jobs
that don’t require being good at
92 84 0.70
Video games beat interviews to re-
cruit the very best
129 122 0.67
1Polarity computed by VADER, with scores ranging from -1 (most negative)
to 1 (most positive).
which indexes all of Hacker News, to retrieve JSON-formatted
topics containing the “interview.” The results were sorted by
popularity, and a limitation of this search engine is that it
returns a maximum of 1,000 results—which it did.
Data cleaning. We automatically ﬁltered the results with
some standard heuristics, such as “interview with,” as these
topics tend to be about interviews with people, not about the
activity of technical interviews. This procedure removed 262
topics from consideration. Two authors then independently
went through the remaining topics manually, and, using the
title alone, excluded topics that were not related to technical
interviews (Cohen’s κ= 1; these topics are easy to identify
but difﬁcult to write a systematic expression for, for example,
“Interviewing my mother, a mainframe COBOL programmer,”
or “AT&T CEO interrupted by a robocall during a live inter-
view”). After data cleaning, 456 topics remained, containing
a total of 46,115 comments.
Characterizing the data on technical interviews. The
relevant topics on technical interviews spanned the time period
from March 3, 2008 through April 25, 2019. The least popular
topic had 29 points (essentially, votes), and the most popular
topic had 1020 points (u= 146,sd = 151). The number
of comments per topic ranged from 0 to 997 (u= 101,
sd = 104). Points also strongly correlate with comments
(r= 0.78), such that more popular topics also tend to have
more comments. To get a high-level sense of the diversity of
topics on technical interviews within the Hacker News com-
munity, Table I presents a list of the most polarizing topics.
To obtain this list, we applied rule-based sentiment analysis
tool, called VADER , to the titles of the Hacker News
topics. 86 of the titles had a positive polarity (greater than 0
and less than or equal to 1, “Best interview questions to spot
ideal employees”), and 98 of the titles had a negative polarity
(less than 0 and greater than or equal to -1, “Programmers
are confessing their sins to protest a broken job interview
process”). For the remaining 272 posts, the VADER did not
identify a polarity in either direction (0, “The GitHub Job
Interview”). In short, the Hacker News community has quite
a bit to say about technical interviews.
Qualitative analysis. We imported the Algolia JSON com-
ments for the top ﬁve topics by posted comments (or ap-
proximately 10% of the data in a Pareto distribution) into
the ATLAS.ti data analysis software.4The topics are: “The
latest trend for tech interviews: Days of unpaid homework”
(997), “I interviewed at six top companies in Silicon Valley
in six days” (692 comments), “How to Pass a Programming
Interview” (552 comments), “I will not do a tech interview”
(545 comments), “‘Clean your desk’: My Amazon interview
experience” (509 comments).
We conducted coding over multiple iterations. In the ﬁrst
cycle, we used descriptive coding, and assigned short codes
and labels to capture and summarize the salience of the
comments . We framed these comments through “small
stories” —an epistemological lens that permits analysis
of small vignettes of everyday stories and experiences—such
as Hacker News comments . In the second iteration, we
conducted a thematic analysis to organize the comments into
To further characterize the themes, we performed an addi-
tional purposive sampling, or non-probabilistic sampling, on
comments in technical interview threads from Hacker News
and authored memos . These memos captured interesting
exchanges that promoted depth and credibility of the concerns,
and framed the posters concerns through their self-reported
experiences. That is, the memos provide a thick description to
contextualize the ﬁndings .
Supporting veriﬁcation. If using a supported PDF reader,
quotations from Hacker News are hyperlinked as HNid and
can be clicked to take the reader to the corresponding Hacker
III. CONCERNS FROM CAND IDATES
In this section, we present expressions of concern about
technical interviews from candidates, organized through
themes. The complete list of concerns is found in Table II.
5Manually, this is equivalent to the URL https://news.ycombinator.com/
item?id=?, with ? substituted with the comment identiﬁer.
THE CONCERNS OF SO FTWAR E CAN DIDAT ES
Concerns about... Description
REL EVANC E
Problem-solving is not grounded in real-world
code, constraints, or scenarios.
ANX IET Y
Stress associated with problem-solving in con-
junction with time pressure and surveillance of
Emotions, such as frustration, and humiliation
associated with the technical interview experi-
AFFO RDAN CE S
A lack of naturally occurring resources and
mechanisms typically available in a normal pro-
gramming environment. For example, coding
with a marker or in word document.
PRAC TIC E
Time commitment needed to practice (“grind”)
various problems and solutions in order to be
competitive with other candidates.
Evaluation criteria and proxies that ﬁlter out
“Building a great and useful app rarely requires Herculean
feats of logic and puzzle solving,” says HN18944553. Indeed,
adds HN18945198, “the number of times I’ve seen things like
dynamic programming come up in a real world application
are vanishingly small.” HN18944499 describes an interview
experience: “I’m a data scientist, and Google asked me to
sum all values of nodes at each height of a tree. I had to
implement the tree, bfs, and the algo (which was easy once
you have bfs) in a 25 minutes, minus any talky time. BFS is
not something I thought about much in the last 5 years, and
quite frankly could care less about. I got stuck when I knew I
needed 'something' to ﬁnish implementing BFS, but couldn’t
remember and the google interviewer offered no help.”
These expressions capture the discrepancy between the
skills needed for performing a software development job and
the problems they are asked to be solved to get that job.
HN18946686 elaborates, “This then becomes representative of
your experience in spite of that fact that you are never likely
to be confronted with that kind of problem with that kind of
time-frame. Whatever is on your CV, and whatever you can
say about what you’ve learned over the years, becomes totally
irrelevant in the face of that.”
A noteworthy undercurrent we found within the discussion
of relevance is view that developers should be able to solve
technical interview problems “from scratch” or view them as
enjoyable: “If you’re a programmer, you like to solve puzzles,”
says HN11248511.HN18944989 explains their belief, “I wrote a
custom HashMap for Java, used BFS and DBS on graphs,
wrote a top down custom parser, custom string searching
algorithms to solve real business problems. I would rather
work with someone who knows how neural networks really
work rather than who knows pytorch or keras, because they
can be learned rather easily. In some industries knowledge of
algorithms can be more valuable than knowing a myriad of
frameworks that change every few years any way.” Finally,
HN18944164 offers that relevance could vary based on expe-
rience: “There are career stages. For candidates just out of
school or with little experience asking algorithmic questions
totally makes sense. For more senior candidates (who actually
progressed to the next stage, not just spent a lot of years), the
questions become more real-life, more open ended and with
more than one (or sometimes none) correct answer.”
“The problem is that interviews are high stress affairs,” says
HN6251756. They continue, “this is great when you’ve got to
climb a tree to get away from a tiger. This is horrible if you
are trying to demonstrate your ability to function mentally. It
does not matter how reasonable your questions are. If this is
what someone faces, you do not get an accurate picture of how
good anyone with interview anxiety is. And a lot of people
suffer from this.” HN6253596 also describes anxiety that arose
during an interview with their own team, “I once interviewed
for a job within the company I already worked for [with] my
existing boss, and a colleague.” Then, “I was asked a question
about ASP.NET ViewState which I drew a complete blank on.
I just couldn’t wrap my ahead around the question because of
nerves. It was only when my boss reminded me that this was
something I had actually taught him and my colleague only 6
months ago that my nerves cleared, I relaxed, and then I could
suddenly think clearly again.”
Having stress can cause disruptive effects on memory ,
, ,  which can negatively affect candidates’ per-
formance during interviews, even with people they already
know: “The interview process creates a dynamic relationship
between interviewer and interviewee that never exists between
colleagues and bosses,” continues HN6253596. One possible
cause of anxiety during the interview is being watched and
judged by the interviewer. HN6252459 says, “I also get very
anxious during exams and always make silly mistakes. It
saddens me to see some interviewers on this thread that naively
believe that the kind of stress a person feels during an exam
(especially one where the tester is sitting in front of you and
watching your every move) is the same kind of stress you
might experience while trying to ﬁx some problem in front of
your computer.” Some developers were not sympathetic with
the concern of anxiety and HN6253513 believes the problem is
ﬁxable, “I think the real problem is in your head: your anxiety
about job interviews is sabotaging something you’re otherwise
perfectly good at,” and continues by adding “You need to be
more zen about this. More laid back, relaxed, conﬁdent, or
something like that.”
“I think it’s offensive and I don’t like how the industry has
standardized on basically assuming everyone’s a bullshitter,”
says HN18946686, adding, “What’s worse is that you’ll have
to repeat this over and over again for any company you
interview with.” Sometimes candidates see themselves as being
a gladiator ﬁghting in the Colosseum for entertainment.“But
made-up puzzles? For which the asker already knows the
answer, so they sit back and watch us dance?,” said HN1260547 .
Candidates feel that the companies try to negatively affect
their self esteem. They believe that some of the companies
convey that their standards are way higher than candidates
abilities. HN11248119 complains about this with saying: “I
have come to believe it is part of an industry-wide negging
style to keep people in their place.” HN11251499 also shares a
related story, “it is like some companies like to feel special.
After being invited twice for Google interviews, I mean really
invited by their HR, not me applying for them. On both
occasions I failed the process with their stupid questions. I
started replying to their HR, if I am so good to be invited but
on their eyes unable to devise a inode search algorithm for
unlimited hard disk sizes with a speciﬁc set of hardware and
search time constraints, over the phone interview, then why
couldn’t they just please stop inviting me!? That was the last
time I heard from them and I don’t care a bit about it.”
Candidates also report concerns with unveiled emotion from
interviewers as well as casual and open disinterest. “I was
literally asked, ‘What is the time complexity of the moving
window average algorithm over an array?’ and when I asked
for clariﬁcation, I could hear an edge of... I guess frustration
in my interviewer’s voice. Granted, by this time, we’d been
through a couple of other problems, and time was running
short, but I still think it was pretty unprofessional of the
interviewer to let frustration or any other sort of negative
emotion show during the interview,” says HN11251441. “The
thing I found strangest is that some interviewers would walk
in the room and throw up a coding exercise without any
introduction at all. They literally wouldn’t give their names and
what projects they worked on” (HN6245567). However, some
candidates also have had happy experiences. “I’ve interviewed
for Google and Apple internships and the process has been
extremely pleasant, with the interviewers happy to give their
time. With Apple, I got to meet the entire team and spend
time with them. I’ve heard similar stories about the Microsoft
interview process (I mean, they even ﬂy you out to Redmond)”
“Expecting perfectly correct code on a whiteboard seems
to me to be a slight abuse of the medium. Whiteboards
and chalkboards speciﬁcally exist to sketch things out in
an adhoc fashion, often in a collaborative and easy-to-edit
way,” says HN1247743. Another developer adds, “every time an
interviewer has told me something like this, they then nitpick
syntax and appear to be primarily concerned with ‘does my
whiteboard code compile’ sorts of problems” (HN17728663).
Affordance issues also extended to interviews over the phone.
“I’ve had that experience during a technical phone screen with
a different ‘hires only the best’ company. I was asked to write
(over the phone) a trivial statistical algorithm and started to
describe the algorithm: ‘Function F returns a double and has
two parameters, pointer to the start of the double array P and
integer N for length of array.’ Apparently on the other end of
the line was a human compiler that kept rejecting my input
and preferred ‘double F open parens double star P comma int
n close parens’!” (HN17727794).
Candidates were concerned that mediums such as white-
boards, shared Google documents, and phone communication
did not take advantage of their skills built over many years
typing in computers and IDEs, with aids such as syntax
highlighting, and auto-completion. “I wouldn’t pass then since
I live in post 2000 and am used to let the IDE handle
the nitty gritty details while I focus on the actual meat of
creating software,” says HN11247663.HN11247490 adds, “This
happened to a friend. He conﬁrmed that pseudocode would be
acceptable, but then as he was writing it out the interviewer got
on him about not terminating lines with semicolons (I suppose
the pseudocode looked C-ish).”
Some developers try to circumvent this concern. HN11247779
suggests, “You can always preempt the whiteboard issue by
bringing a laptop along. Hey, I’m a lot more comfortable
writing code on a keyboard and with an IDE. Lets program
this together in a text editor instead of a whiteboard.” But,
HN11247895 cautions, “Speaking as an interviewer, don’t do
this to me without prior discussion. Being able to discuss
things on a whiteboard is a necessary skill for working in
a co-located ofﬁce. This includes pseudocode.”
“It’s not about getting the right answer but the way you
think. I’ve never found that to be true. If you don’t get to the
right answer, you’re gone. If they planned to ask two questions
and you only got through one, you’re gone no matter how you
‘thought’ about it. A huge part of this is Leetcode practice.
If you can’t solve most algorithm questions on whiteboard in
less than an hour (because you haven’t practiced) then you
won’t pass any interviews,” warns HN17729934.
Candidates were concerned about the time commitment
required to practice and/or memorize algorithms. “Now that
I am 51, I feel annoyed that all of these stories of interviews
involve asking questions about algorithms that rarely come up
in real coding,” laments HN18944082, “I cannot spend hours
and hours studying up on these algorithms, there are much
more important things (real coding-related things) which I
need to learn about, to the extent I have time to do that.”
HN11249643 adds, “To me it was a bunch of rote memorization,
just like a biology course. I never - never - have needed
to know how bubblesort/heapsort/mergesort actually work,
except to appease interviewers.”
Candidates highlighted the potential bias associated with
availability to practice. “Honestly, there are so many posts
like yours on HN, it’s a surprise companies don’t change this
ridiculous algorithms thing,” questions HN18944244, “However,
algorithms bias towards younger people, recently out of col-
lege, math hobbyists and people with a lot of free time,”
HN18944519 adds, “They used to interview using the kind of
brainteasers found in books like the ones Mensa used to make.
The algorithms approach, I suspect, is just a CS proxy for an
test just like their old approach was. It would also ﬁlter for
youth, which they semi openly advertise as well (see chess
literature on brain age for what I mean). Conformance too
(due to the prep time).”
Finally, HN11251399 warns about the dangers of hiring
developers based on ability to practice: “Even once you get
past the outright bozos, there are quite a few programmers who
can program quick one-off things, but have no sense of design
or maintainability. They can deliver functionality, but deliver
in a way that piles on technical debt and damages the long
term health of the codebase. I think the traditional technical
interview format ironically encourages this sort of behavior,
by encouraging applicants to focus on narrowly solving the
problem at hand, as quickly as possible, both in terms of
machine time and programmer time, even if that means the
code is an unmaintainable mess in the long run.”
F. Missing out
“I used to work at Google. I saw a lot of good candidates
get rejected. I myself was rejected multiple times before I got
an offer. I was talking to my manager who was on the Hiring
Committee about this dilemma, and at the end of the day the
fact is that good companies don’t give a shit about their false
negative rate - only their net positives. By having an efﬁcient
technical interview process, yes, you let good candidates go.
Just as you do by only having certain target universities or
requiring certain experience. But they don’t give a fuck. They
get 1000 applications a day. Hundreds of internal referrals.”
Developers frequently shared stories where candidates
would have normally been ﬁltered out if not for considerable
interventions: HN12860682 shares their experience, “ I have a
buddy who I have dragged along with me (many times staking
my reputation on his abilities), to every engagement I go on
and this guy could not pass a How to use Microsoft Word
interview. He has Aspergers and locks up and fails miserably
in the interviewing process, but the honest, reality is, he is
10 times the developer I am, the guy sees patterns instantly
and has a knack for code organization. He can master a new
technology in a week and is hands down the best developer
I have ever met. That being said, over the years watching
him has lead me to the conclusion that it is indeed the ones
they hold faith in the technical interview are actually the ones
that are the most stove piped and only see the world thru
their limited experiences. It should be classiﬁed as a form of
Developers also wondered how much the current practices
might be ﬁltering out candidates with more diverse back-
grounds and skills: “I cannot help but think that these big tech
companies (FAANG, et. al) are missing out on diversifying and
increasing their engineering expertise by passing over develop-
ers like you. I often think what would Google/Facebook would
be like if they hired in some experienced engineers that may
not be able to whiteboard a BFS tree or can tell you Djikstra’s
algorithm, but have proven business track records of getting
projects done, on budget, and on time. Real, pragmatic, get-it-
done types of engineers. (That’s not to say whiteboard expert
engineers can’t also be this way - it’s just that whiteboard
interviews don’t hire for this in particular - technical expertise
comes ﬁrst).” (HN18943168). HN18944016 shares never being
able to ﬁnd gender parity in interviews: “Also... I mentioned
that working on teams with other women was important to
me... but every technical onsite I’ve had has been given
by a man. They’ve pitched teams led by women, and my
HR/recruiting contacts have been nearly all women. But for
the interview itself? All men.”
Conventional wisdom, as reﬂected in “Cracking the Code
Interview,” has claimed that hiring a false positive (bad hire)
is much more expensive than a false negative (missing a
good hire) . HN18945043 adds, “The top companies with
these leetcode tests probably don’t care that good people
being rejected or avoiding them because of the amount of
preparation required. Middle sized companies and startups
doing leetcode tests are missing good people and probably
can’t afford the same number of false negatives as someone
like Google with an endless supply of candidates.” Several
developers, including HN18944154, offered counter-arguments
to conventional wisdom: “You think people that pass technical
interviews can’t be false positives? I think they weed out a
few, but completely ignore practical development skills, work
ethic, soft skills, design and architecture skills, etc. Of course
maybe this explains why most of the big tech companies have
seemed pretty stagnant for the last decade, largely failing with
products and decisions that have poor execution and market ﬁt
outside of the products that made them big in the ﬁrst place.”
The nature of small stories analysis, and our application of
it to Hacker News, introduces several limitations.
Representativeness. Our study into the technical interviews
were conducted through an analysis of a single source of
practitioner experiences, Hacker News. There are some sub-
stantial biases in terms of demographics6. Speciﬁcally, in a
survey conducted in 2011 with 4643 respondents, 89% self-
reported being under the age of 40, with 43% of respondents
being between the ages of 26-30. In a similar gender survey
conducted in 2009 with 1487, 95% of respondents reported as
male. We did not ﬁnd any demographic information on race.
The implication of this demographic is that it may not
accurately reﬂect the concerns of a more diverse population,
particularly with respect to underrepresented minorities. Nev-
ertheless, our ﬁndings in some ways reﬂects a lower bound on
the concerns of developers. If non-marginalized groups have
substantial concerns about technical interviews, then it is very
likely that the impact of technical interviews on marginalized
groups is even more severe.
Groupthink in online communities. Another effect from
online communities may arise as a result of the moderation and
points-system used within Hacker News to rank and display
comments, in which individuals in the community internalize
their true opinions and instead converge to a form of group-
think. Fearing reprisal from other members of the community,
individuals may be compelled to only share experiences that
they believe would be positively scored by their peers .
It is also possible that other communities may have different
perspectives than Hacker News. Consequently, the set of
identiﬁed concerns may not be complete.
Qualitative interpretations. Finally, we acknowledge that
qualitative research, however rigorously conducted, involves
not only the qualitative data under investigation but also a
level of subjectivity and interpretation on the part of the
researcher as they frame and synthesize the results of their
inquiry. In particular, though many posters express concerns
about technical interviews, posters whose thoughts are better
articulated tend to be given greater representation in the
results. Thus, we emphasize that our own ﬁndings should be
examined as only one of many possible presentations with
respect to technical interviews.
Additional studies are needed to mitigate these limitations,
including additional interviews, surveys, and other instruments
to triangulate our concerns. Our ﬁndings can be used as a
starting point for conducting such studies.
V. RE LATE D WOR K
Despite their importance, technical interviews are under-
studied in the scientiﬁc literature. Ford and colleagues 
conducted a study from the perspective of hiring managers and
University students participating in mock technical interviews.
The study identiﬁed a mismatch of candidates expectations
between what interviewers assess and what they actually look
for in a candidate—speciﬁcally, through implicit norms in
how interviewers expected candidates to explanation their
solutions, such as using “concrete examples” and “asking
relevant questions.” In contrast, our study in this paper focuses
on professional developers, rather than students.
A slightly distant study by Ford and colleagues  iden-
tiﬁed barriers for female participants in Stack Overﬂow, an
online programming community. Although not conducted in
a technical interview setting, several of the identiﬁes barriers
resonate with those we identiﬁed in our study, in particular,
impersonal interactions such as fear of negative feedback,
discomfort from lack of diversity in the interviewers, an im-
poster syndrome of feeling that they didn’t have the necessary
expertise or qualiﬁcations, and time constraints that prevented
investment in the site beyond their work day. A survey study
with both male and female developers conﬁrmed these barriers
as being common across genders.
Using head-mounted eye trackers, Behroozi and colleagues
conducted a preliminary study of the public whiteboard in-
terview setting and found that this setting pressures candi-
dates into keeping shorter attention lengths and experienc-
ing higher levels of cognitive load compared to solving the
same problems on privately on paper. The paper concludes
that “programming is a cognitive intensive task that deﬁes
expectations of constant feedback that todays interview pro-
cesses follow.”  Zhou and colleagues  investigated both
technical and social competencies through GitHub and Stack
Overﬂow data dumps. They found a collaboration competency
skills are strongly associated with enhanced coding abilities as
well as the quality of code.
Wyrich and colleagues  conducted an exploratory qual-
itative study with 32 software engineering students and found
that coding challenge solvers also have better exam grades
and more programming experience. Moreover, conscientious
as well as sad software engineers performed worse.
Our study complements this prior work by offering quali-
tative context that explains technical interview performance.
Examining the grey literature of software—that is, non-
published, nor peer-reviewed sources of practitioners—
provides some additional, though contradictory insights.
Lerner  conducted a study over a thousand interviews
using the interviewing.io platform, where developers can
practice technical interviewing anonymously. Their signiﬁcant
ﬁnding is that perform from technical interview to interview
is arbitrary, and that interview performance is volatile—only
20% of the interviewees are consistent in their performance,
and the rest are all over the place in terms of their inter-
view evaluation. In contrast, a study conducted at Google
by Shaper  investigated a subset interview data over ﬁve
years to determine the value of an interviewers feedback, and
found that the four interviews were enough to predict whether
someone should be hired at Google with 86% conﬁdence.
Regardless, our study ﬁnds that developers perceive these
interview practices to be arbitrary.
A study by Minor , conducted across eleven ﬁrms
in various industries, reported the desire to minimize false
positives from “toxic hires”—hires who may steal, commit
fraud, bully other workers, or engage in sexual harassment.
They found that “toxic workers are actually much more
productive than the average worker, which can perhaps explain
why they tend to stick around an organization longer than they
should.” Interestingly, the study found that although greater
conﬁdence predicts increased productivity, greater conﬁdence
also predicts greater likelihood of becoming toxic. As found
in our study, this is an important aspect of hiring that is
elided when focusing primarily on the analytical ability of
In this section, we mitigate some of the concerns related
to technical interviews from software developers through a
set of inclusive interview guidelines.
Guideline I—Use rudimentary questions for screening.
When applying for software development positions, candidates
may have limited notice before having to participate in a phone
screening. Consequently, they may not have the time available
to adequately prepare for this interview (Section III-E). The
goal of the phone screen shouldn’t be to fully assess the
candidates’ capabilities. Instead, the interview should be a
rudimentary ﬁlter to assess whether the candidate can program
at all, through what Atwood  describes as “blindingly,
intentionally easy” questions. A second goal of the phone
screen is informational: to share what the team does and to
identify whether the candidates’ advertised skill set ﬁts these
Our suggestion is that algorithms at this stage of the
interview be no more complicated than the Rainfall Problem—
a programming task that has been used in a number of studies
of programming ability , .7
When complex algorithms are requested in interviews,
hiring managers may be unnecessarily excluding candidates
simply because of their time commitments, and not because
of their lack of technical qualiﬁcations (Section III-F). If
hiring managers need further evaluation, they can propose
a small take-home project, where the candidate has more
ﬂexibility and autonomy in how they conduct this work.
Guideline II—Share the interview description in advance.
To make technical interviews more equitable for all candidates,
the hiring manager should share the details of the interview
procedure with them. This includes not only the length of the
interview, but also the types of questions that they will be
asked. If certain resources are useful for being successful in
the interview, these should be recommended to the candidate.
Importantly, the scoring rubric for the technical interview
should not be opaque to the candidate. Opaque hiring criteria
gives an unfair advantage to those with prior interviewing
experience, and can be frustrating to candidates who are
unaware of the otherwise unwritten rules particular to the
organization (Section III-C) .
Guideline III—Offer alternative interview formats. Allow
candidates to opt-out of certain interview formats or make mi-
nor adjustments to the format, without reducing their ability to
assess problem-solving skills. For example, a simple change to
existing interview format could reduce the anxiety associated
with public performance in front of an interviewer, for exam-
ple, by offering the candidate the opportunity to initially think
about the problem in private  (Section III-B). Moreover,
having to think-aloud while performing a cognitively demand-
ing task has been shown to inhibit task performance .
Similarly, while some candidates may prefer conducting
the technical interview on a whiteboard, others may feel
more comfortable working within an integrated development
environment on the computer, or ﬁnd it more natural to explain
and sketch a problem on pencil-and-paper  (Section III-D).
Guideline IV—Use a real problem. Several comments on
Hacker News were critical of the use of artiﬁcial, puzzle-
like problems that did not reﬂect the types of engineering
tasks that candidates would do in their day-to-day software
development activities (Section III-A). Such problems were
also perceived as biased towards junior candidates just out of
school, as these junior candidates were more likely to have
recently solved these kinds of homework-style problems.
For senior developers, our suggestion is to use technical
7The original wording of this problem is simple enough, though variations
exist: “Write a program that will read in integers and output their average.
Stop reading when the value 99999 is input” .
interview problems that have real-world scenarios, and
resemble programs that the candidate would actually write as
a software developer within the team. For example, consider
the purely academic problem of performing a depth-ﬁrst
traversal of a graph structure versus framing this problem as
determining the order in which to install packages from a
package management system when provided with an input
of a hierarchy of dependencies. In the latter scenario, it may
even be sufﬁcient that the candidate can simply identify that
this problem is an instance of depth-ﬁrst search, without
needing them to actually write code.
Guideline V—Solve problems as colleagues, not as ex-
aminers. Not all candidates can effectively solve technical
algorithms in a fast-paced, high-pressure whiteboard setting—
nor do these candidate often work in such stressful situations in
their day-to-day software development jobs (Section III-B). In-
stead, the conversation between the interviewer and candidate
should be less of an antagonistic interrogation, and more of a
conversation in which both the candidate and the interviewer
work together to solve the problem. Preferably, the candidate
should be interviewed by the team that they intend to join, so
that they can see experience ﬁrst-hand how they would work
with one or more of their potential teammates.
The technical interview has become commonplace within
the software development industry as a means to assess can-
didates. However, despite its importance for hiring qualiﬁed
candidates, the effectiveness and perceptions of the technical
interview are understudied within the software engineering
To understand how software developers perceive technical
interviews, we conducted qualitative study using the online
social news website, Hacker News. By framing comments
as small stories, we identiﬁed critical concerns from can-
didates regarding these interviews. These concerns include
the relevance of these interviews as well as their impact
on the candidates’ anxiety, affect, and time commitments.
We ﬁnd that candidates who use technical interviews as a
primary assessment instrument may unfairly ﬁlter out oth-
erwise qualiﬁed candidates. We propose inclusive interview
guidelines towards improving the technical interview process.
The ﬁndings from this study underscore the need for additional
research in this area, especially towards understanding how
technical interviews impact underrepresented minorities within
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