Measuring Developer Productiveness by way of People

Someplace, proper now, a expertise government tells their administrators: “we
want a strategy to measure the productiveness of our engineering groups.” A working
group assembles to discover potential options, and weeks later, proposes
implementing the metrics: lead time, deployment frequency, and variety of
pull requests created per engineer.

Quickly after, senior engineering leaders meet to overview their newly created
dashboards. Instantly, questions and doubts are raised. One chief says:
“Our lead time is 2 days which is ‘low performing’ in keeping with these
benchmarks – however is there really an issue?”. One other chief says: “it’s
unsurprising to see that a few of our groups are deploying much less typically than
others. However I’m undecided if this spells a possibility for enchancment.”

If this story arc is acquainted to you, don’t fear – it is acquainted to
most, together with a few of the largest tech corporations on the earth. It’s not unusual
for measurement packages to fall quick when metrics like DORA fail to offer
the insights leaders had hoped for.

There’s, nevertheless, a greater strategy. An strategy that focuses on
capturing insights from builders themselves, quite than solely counting on
primary measures of velocity and output. We’ve helped many organizations make the
leap to this human-centered strategy. And we’ve seen firsthand the
dramatically improved understanding of developer productiveness that it

What we’re referring to right here is qualitative measurement. On this
article, we offer a primer on this strategy derived from our expertise
serving to many organizations on this journey. We start with a definition of
qualitative metrics and advocate for them. We comply with with sensible
steerage on seize, observe, and make the most of this knowledge.

At present, developer productiveness is a important concern for companies amid
the backdrop of fiscal tightening and transformational applied sciences reminiscent of
AI. As well as, developer expertise and platform engineering are garnering
elevated consideration as enterprises look past Agile and DevOps
transformation. What all these issues share is a reliance on measurement
to assist information choices and observe progress. And for this, qualitative
measurement is essential.

Notice: once we say “developer productiveness”, we imply the diploma to which
builders’ can do their work in a frictionless method – not the person
efficiency of builders. Some organizations discover “developer productiveness”
to be a problematic time period due to the best way it may be misinterpreted by
builders. We advocate that organizations use the time period “developer
expertise,” which has extra constructive connotations for builders.

What’s a qualitative metric?

We outline a qualitative metric as a measurement comprised of knowledge
offered by people. It is a sensible definition – we haven’t discovered a
singular definition inside the social sciences, and the choice
definitions we’ve seen have flaws that we focus on later on this

Determine 1: Qualitative metrics are measurements derived from people

The definition of the phrase “metric” is unambiguous. The time period
“qualitative,” nevertheless, has no authoritative definition as famous within the
2019 journal paper What is Qualitative in
Qualitative Research

There are a lot of definitions of qualitative analysis, but when we search for
a definition that addresses its distinctive characteristic of being
“qualitative,” the literature throughout the broad subject of social science is
meager. The principle cause behind this text lies within the paradox, which, to
put it bluntly, is that researchers act as in the event that they know what it’s, however
they can’t formulate a coherent definition.

An alternate definition we’ve heard is that qualitative metrics measure
high quality, whereas quantitative metrics measure amount. We’ve discovered this
definition problematic for 2 causes: first, the time period “qualitative
metric” consists of the time period metric, which suggests that the output is a
amount (i.e., a measurement). Second, high quality is often measured
via ordinal scales which are translated into numerical values and
scores – which once more, contradicts the definition.

One other argument we now have heard is that the output of sentiment evaluation
is quantitative as a result of the evaluation ends in numbers. Whereas we agree
that the info ensuing from sentiment evaluation is quantitative, primarily based on
our authentic definition that is nonetheless a qualitative metric (i.e., a amount
produced qualitatively) except one have been to take the place that
“qualitative metric” is altogether an oxymoron.

Except for the issue of defining what a qualitative metric is, we’ve
additionally encountered problematic colloquialisms. One instance is the time period “gentle
metric”. We warning in opposition to this phrase as a result of it harmfully and
incorrectly implies that knowledge collected from people is weaker than “laborious
metrics” collected from programs. We additionally discourage the time period “subjective
metrics” as a result of it misconstrues the truth that knowledge collected from people
could be both goal or subjective – as we focus on within the subsequent

Qualitative metrics: Measurements derived from people
Kind Definition Instance
Attitudinal metrics Subjective emotions, opinions, or attitudes towards a selected topic. How glad are you together with your IDE, on a scale of 1–10?
Behavioral metrics Goal info or occasions pertaining to a person’s work expertise. How lengthy does it take so that you can deploy a change to manufacturing?

Later on this article we offer steerage on accumulate and use
these measurements, however first we’ll present a real-world instance of this
strategy put to observe

Peloton is an American expertise firm
whose developer productiveness measurement technique facilities round
qualitative metrics. To gather qualitative metrics, their group
runs a semi-annual developer expertise survey led by their Tech
Enablement & Developer Expertise workforce, which is a part of their Product
Operations group.

Thansha Sadacharam, head of tech studying and insights, explains: “I
very strongly consider, and I feel numerous our engineers additionally actually
recognize this, that engineers aren’t robots, they’re people. And simply
taking a look at primary numbers does not drive the entire story. So for us, having
a very complete survey that helped us perceive that complete
developer expertise was actually necessary.”

Every survey is shipped to
a random pattern of roughly half of their builders. With this strategy,
particular person builders solely must take part in a single survey per 12 months,
minimizing the general time spent on filling out surveys whereas nonetheless
offering a statistically important consultant set of knowledge outcomes.
The Tech Enablement & Developer Expertise workforce can also be liable for
analyzing and sharing the findings from their surveys with leaders throughout
the group.

For extra on Peloton’s developer expertise survey, listen to this

with Thansha Sadacharam.

Advocating for qualitative metrics

Executives are sometimes skeptical in regards to the reliability or usefulness of
qualitative metrics. Even extremely scientific organizations like Google have
needed to overcome these biases. Engineering leaders are inclined towards
system metrics since they’re accustomed to working with telemetry knowledge
for inspecting programs. Nevertheless, we can not depend on this similar strategy for
measuring folks.

Keep away from pitting qualitative and quantitative metrics in opposition to one another.

We’ve seen some organizations get into an inner “battle of the
metrics” which isn’t a great use of time or power. Our recommendation for
champions is to keep away from pitting qualitative and quantitative metrics in opposition to
one another as an both/or. It’s higher to make the argument that they’re
complementary instruments – as we cowl on the finish of this text.

We’ve discovered that the underlying reason for opposition to qualitative knowledge
are misconceptions which we tackle beneath. Later on this article, we
define the distinct advantages of self-reported knowledge reminiscent of its capacity to
measure intangibles and floor important context.

False impression: Qualitative knowledge is barely subjective

Conventional office surveys sometimes give attention to the subjective
opinions and emotions of their staff. Thus many engineering leaders
intuitively consider that surveys can solely accumulate subjective knowledge from

As we describe within the following part, surveys may seize
goal details about info or occasions. Google’s DevOps Research and
Assessment (DORA)
program is a wonderful concrete

Some examples of goal survey questions:

  • How lengthy does it take to go from code dedicated to code efficiently
    working in manufacturing?
  • How typically does your group deploy code to manufacturing or
    launch it to finish customers?

False impression: Qualitative knowledge is unreliable

One problem of surveys is that individuals with all method of backgrounds
write survey questions with no particular coaching. Consequently, many
office surveys don’t meet the minimal requirements wanted to supply
dependable or legitimate measures. Properly designed surveys, nevertheless, produce
correct and dependable knowledge (we offer steerage on how to do that later in
the article).

Some organizations have issues that individuals could lie in surveys. Which
can occur in conditions the place there may be concern round how the info can be
used. In our expertise, when surveys are deployed as a device to assist
perceive and enhance bottlenecks affecting builders, there isn’t any
incentive for respondents to lie or recreation the system.

Whereas it’s true that survey knowledge isn’t at all times 100% correct, we regularly
remind leaders that system metrics are sometimes imperfect too. For instance,
many organizations try to measure CI construct instances utilizing knowledge aggregated
from their pipelines, solely to seek out that it requires important effort to
clear the info (e.g. excluding background jobs, accounting for parallel
jobs) to supply an correct outcome

The 2 forms of qualitative metrics

There are two key forms of qualitative metrics:

  1. Attitudinal metrics seize subjective emotions, opinions, or
    attitudes towards a selected topic. An instance of an attitudinal measure would
    be the numeric worth captured in response to the query: “How glad are
    you together with your IDE, on a scale of 1-10?”.
  2. Behavioral metrics seize goal info or occasions pertaining to an
    people’ work experiences. An instance of a behavioral measure can be the
    amount captured in response to the query: “How lengthy does it take so that you can
    deploy a change to manufacturing?”

We’ve discovered that almost all tech practitioners overlook behavioral measures
when desirous about qualitative metrics. This happens regardless of the
prevalence of qualitative behavioral measures in software program analysis, such
because the Google’s DORA program talked about earlier.

DORA publishes annual benchmarks for metrics reminiscent of lead time for
adjustments, deployment frequency, and alter fail price. Unbeknownst to many,
DORA’s benchmarks are captured utilizing qualitative strategies with the survey
objects proven beneath:

Lead time

For the first software or service you’re employed on,
what’s your lead time for adjustments (that’s, how lengthy does it take to go
from code dedicated to code efficiently working in manufacturing)?

Greater than six months

One to 6 months

One week to at least one month

Someday to at least one week

Lower than someday

Lower than one hour

Deploy frequency

For the first software or service you
work on, how typically does your group deploy code to manufacturing or
launch it to finish customers?

Fewer than as soon as per six months

Between as soon as per thirty days and as soon as each six months

Between as soon as per week and as soon as per thirty days

Between as soon as per day and as soon as per week

Between as soon as per hour and as soon as per day

On demand (a number of deploys per day)

Change fail proportion

For the first software or service you’re employed on, what
proportion of adjustments to manufacturing or releases to customers end in
degraded service (for instance, result in service impairment or service
outage) and subsequently require remediation (for instance, require a
hotfix, rollback, repair ahead, patch)?







Time to revive

For the first software or service you’re employed on, how lengthy
does it usually take to revive service when a service incident or a
defect that impacts customers happens (for instance, unplanned outage, service

Greater than six months

One to 6 months

One week to at least one month

Someday to at least one week

Lower than someday

Lower than one hour

We’ve discovered that the flexibility to gather attitudinal and behavioral knowledge
on the similar time is a strong advantage of qualitative measurement.

For instance, behavioral knowledge may present you that your launch course of
is quick and environment friendly. However solely attitudinal knowledge might inform you whether or not it
is easy and painless, which has necessary implications for developer
burnout and retention.

To make use of a non-tech analogy: think about you’re feeling sick and go to a
physician. The physician takes your blood stress, your temperature, your coronary heart
price, and so they say “Properly, it seems to be such as you’re all good. There’s nothing
mistaken with you.” You’d be greatly surprised! You’d say, “Wait, I’m telling
you that one thing feels mistaken.”

The advantages of qualitative metrics

One argument for qualitative metrics is that they keep away from subjecting
builders to the sensation of “being measured” by administration. Whereas we’ve
discovered this to be true – particularly when in comparison with metrics derived from
builders’ Git or Jira knowledge – it doesn’t tackle the primary goal
advantages that qualitative approaches can present.

There are three fundamental advantages of qualitative metrics relating to
measuring developer productiveness:

Qualitative metrics help you measure issues which are in any other case

System metrics like lead time and deployment quantity seize what’s
taking place in our pipelines or ticketing programs. However there are various extra
points of builders’ work that should be understood so as to enhance
productiveness: for instance, whether or not builders are capable of keep within the circulation
or work or simply navigate their codebases. Qualitative metrics allow you to
measure these intangibles which are in any other case troublesome or unattainable to

An attention-grabbing instance of that is technical debt. At Google, a research to
establish metrics for technical debt included an evaluation of 117 metrics
that have been proposed as potential indicators. To the frustration of
Google researchers, no single metric or mixture of metrics have been discovered
to be legitimate indicators (for extra on how Google measures technical debt,
listen to this interview).

Whereas there could exist an undiscovered goal metric for technical
debt, one can suppose that this can be unattainable on account of the truth that
evaluation of technical debt depends on the comparability between the present
state of a system or codebase versus its imagined ideally suited state. In different
phrases, human judgment is crucial.

Qualitative metrics present lacking visibility throughout groups and

Metrics from ticketing programs and pipelines give us visibility into
a few of the work that builders do. However this knowledge alone can not give us
the complete story. Builders do numerous work that’s not captured in tickets
or builds: for instance, designing key options, shaping the path of a
venture, or serving to a teammate get onboarded.

It’s unattainable to achieve visibility into all these actions via
knowledge from our programs alone. And even when we might theoretically accumulate
all the info via programs, there are further challenges to capturing
metrics via instrumentation.

One instance is the issue of normalizing metrics throughout totally different
workforce workflows. For instance, should you’re making an attempt to measure how lengthy it takes
for duties to go from begin to completion, you may attempt to get this knowledge
out of your ticketing device. However particular person groups typically have totally different
workflows that make it troublesome to supply an correct metric. In
distinction, merely asking builders how lengthy duties sometimes take could be
a lot easier.

One other frequent problem is cross-system visibility. For instance, a
small startup can measure TTR (time to revive) utilizing simply a problem
tracker reminiscent of Jira. A big group, nevertheless, will probably must
consolidate and cross-attribute knowledge throughout planning programs and deployment
pipelines so as to acquire end-to-end system visibility. This is usually a
yearlong effort, whereas capturing this knowledge from builders can present a
baseline rapidly.

Qualitative metrics present context for quantitative knowledge

As technologists, it’s straightforward to focus closely on quantitative measures.
They appear clear and clear, afterall. There’s a danger, nevertheless, that the
full story isn’t being instructed with out richer knowledge and that this may occasionally lead us
into specializing in the mistaken factor.

One instance of that is code overview: a typical optimization is to attempt to
velocity up the code overview. This appears logical as ready for a code overview
may cause wasted time or undesirable context switching. We might measure the
time it takes for opinions to be accomplished and incentivize groups to enhance
it. However this strategy could encourage detrimental conduct: reviewers dashing
via opinions or builders not discovering the correct specialists to carry out

Code opinions exist for an necessary function: to make sure prime quality
software program is delivered. If we do a extra holistic evaluation – specializing in the
outcomes of the method quite than simply velocity – we discover that optimization
of code overview should guarantee good code high quality, mitigation of safety
dangers, constructing shared information throughout workforce members, in addition to making certain
that our coworkers aren’t caught ready. Qualitative measures may help us
assess whether or not these outcomes are being met.

One other instance is developer onboarding processes. Software program improvement
is a workforce exercise. Thus if we solely measure particular person output metrics such
as the speed new builders are committing or time to first commit, we miss
necessary outcomes e.g. whether or not we’re totally using the concepts the
builders are bringing, whether or not they really feel secure to ask questions and if
they’re collaborating with cross-functional friends.

How you can seize qualitative metrics

Many tech practitioners don’t notice how troublesome it’s to put in writing good
survey questions and design good survey devices. Actually, there are
entire fields of research associated to this, reminiscent of psychometrics and
industrial psychology. It is very important carry or construct experience right here
when potential.

Beneath are few good guidelines for writing surveys to keep away from the most typical
errors we see organizations make:

  • Survey objects should be fastidiously worded and each query ought to solely ask
    one factor.
  • If you wish to evaluate outcomes between surveys, watch out about altering
    the wording of questions such that you simply’re measuring one thing totally different.
  • In case you change any wording, you will need to do rigorous statistical exams.

In survey parlance, ”good surveys” means “legitimate and dependable” or
“demonstrating good psychometric properties.” Validity is the diploma to
which a survey merchandise really measures the assemble you need to measure.
Reliability is the diploma to which a survey merchandise produces constant
outcomes out of your inhabitants and over time.

One mind-set about survey design that we’ve discovered useful to
tech practitioners: consider the survey response course of as an algorithm
that takes place within the human thoughts.

When a person is introduced a survey query, a sequence of psychological
steps happen so as to arrive at a response. The mannequin beneath is from
the seminal 2012 e book, The Psychology of Survey

Parts of the Response Course of
Part Particular Processes

Attend to questions and directions

Symbolize logical type of query

Establish query focus (info sought)

Hyperlink key phrases to related ideas


Generate retrieval technique and cues

Retrieve particular, generic recollections

Fill in lacking particulars


Assess completeness and relevance of recollections

Draw inferences primarily based on accessibility

Combine materials retrieved

Make estimate primarily based on partial retrieval


Map Judgement onto response class

Edit response

Decomposing the survey response course of and inspecting every step
may help us refine our inputs to supply extra correct survey outcomes.
Growing good survey objects requires rigorous design, testing, and
evaluation – similar to the method of designing software program!

However good survey design is only one facet of working profitable surveys.
Extra challenges embody participation charges, knowledge evaluation, and understanding
act on knowledge. Beneath are a few of the finest practices we’ve

Phase outcomes by workforce and persona

A standard mistake made by organizational leaders is to give attention to companywide
outcomes as an alternative of knowledge damaged down by workforce and persona (e.g., position, tenure,
seniority). As beforehand described, developer expertise is very contextual
and may differ radically throughout groups or roles. Focusing solely on combination
outcomes can result in overlooking issues that have an effect on small however necessary
populations inside the firm, reminiscent of cellular builders.

Examine outcomes in opposition to benchmarks

Comparative evaluation may help contextualize knowledge and assist drive motion. For
instance, developer sentiment towards code high quality generally skews detrimental, making
it troublesome to establish true issues or gauge their magnitude. The extra
actionable knowledge level is: “are our builders extra pissed off about code
high quality than different groups or organizations?” Groups with decrease sentiment scores
than their friends and organizations with decrease scores than their trade friends
can floor notable alternatives for enchancment.

Use transactional surveys the place acceptable

Transactional surveys seize suggestions throughout particular touchpoints or
interactions within the developer workflow. For instance, platform groups can use
transactional surveys to immediate builders for suggestions whereas they’re within the midst of
creating a brand new service in an inner developer portal. Transactional surveys can
additionally increase knowledge from periodic surveys by producing higher-frequency suggestions and
extra granular insights.

Keep away from survey fatigue

Many organizations battle to maintain excessive participation charges in surveys
over time. Lack of follow-up may cause builders to really feel that
repeatedly responding to surveys just isn’t worthwhile. It’s due to this fact
important that leaders and groups comply with up and take significant motion after surveys.
Whereas a quarterly or
semi-annual survey cadence is perfect for many organizations, we’ve seen some
organizations achieve success with extra frequent surveys which are built-in into
common workforce rituals reminiscent of retrospectives.

Survey Template

Beneath are a easy set of survey questions for getting began. Load the questions
beneath into your most popular survey device, or get began rapidly by making a replica of our ready-to-go
Google Forms template.

The template is deliberately easy, however surveys typically develop into fairly sizable as your measurement
technique matures. For instance, Shopify’s developer survey is 20-minutes
lengthy and Google’s is over 30-minutes lengthy.

After you have collected responses, rating the a number of selection questions
utilizing both imply or high field scoring. Imply scores are calculated by
assigning every choice a worth between 1 and 5 and taking the common.
High field scores are calculated by the odds of responses that
select one of many high two most favorable choices.

You’ll want to overview open textual content responses which might include nice
info. In case you’ve collected numerous feedback, LLM instruments
reminiscent of ChatGPT could be helpful for extracting core themes and
strategies. Once you’ve completed analyzing outcomes, remember to share
your findings with respondents so their time filling out the survey
feels worthwhile.

How straightforward or troublesome is it so that you can do work as a
developer or technical contributor at [INSERT ORGANIATION NAME]?

Very troublesome

Considerably troublesome

Neither straightforward nor troublesome

Considerably straightforward

Very straightforward

For the first software or service you’re employed on, what
is your lead time for adjustments (that’s, how lengthy does it take to go
from code dedicated to code efficiently working in

A couple of month

One week to at least one month

Someday to at least one week

Lower than someday

Lower than one hour

How typically do you are feeling extremely productive in your

By no means

Somewhat of the time

A few of the time

More often than not

The entire time

Please price your settlement or disagreement with the next

My workforce follows improvement finest practices
I’ve sufficient time for deep work.
I’m glad with the quantity of automated check protection in
my venture.
It is easy for me to deploy to manufacturing.
I am glad with the standard of our CI/CD tooling.
My workforce’s codebase is simple for me to contribute to.
The quantity of technical debt on my workforce is suitable primarily based on our targets.
Specs are repeatedly revisited and reprioritized in keeping with person indicators.

Please share any further suggestions on how your developer expertise could possibly be improved

[open textarea]

Utilizing qualitative and quantitative metrics collectively

Qualitative metrics and quantitative metrics are complementary approaches
to measuring developer productiveness. Qualitative metrics, derived from
surveys, present a holistic view of productiveness that features each subjective
and goal measurements. Quantitative metrics, alternatively, present
distinct benefits as effectively:

  • Precision. People can inform you whether or not their CI/CD builds are usually
    quick or sluggish (i.e., whether or not durations are nearer to a minute or an hour), however
    they can’t report on construct instances all the way down to millisecond precision. Quantitative
    metrics are wanted when a excessive diploma of precision is required in our
  • Continuity. Sometimes, the frequency at which a company can survey
    their builders is at most a couple of times per quarter. As a way to accumulate extra
    frequent or steady metrics, organizations should collect knowledge

Finally, it’s via the mixture of qualitative and quantitative metrics – a mixed-methods strategy
that organizations can acquire most visibility into the productiveness and
expertise of builders. So how do you utilize qualitative and quantitative
metrics collectively?

We’ve seen organizations discover success once they begin with qualitative
metrics to determine baselines and decide the place to focus. Then, comply with with
quantitative metrics to assist drill in deeper into particular areas.

Engineering leaders discover this strategy to be efficient as a result of qualitative
metrics present a holistic view and context, offering broad understanding of
potential alternatives. Quantitative metrics, alternatively, are
sometimes solely out there for a narrower set of the software program supply
course of.

Google equally advises its engineering leaders to go to survey knowledge first
earlier than taking a look at logs knowledge because of this. Google engineering researcher
Ciera Jaspan explains: “We encourage leaders to go to the survey knowledge first,
as a result of should you solely take a look at logs knowledge it does not actually inform you whether or not
one thing is nice or unhealthy. For instance, we now have a metric that tracks the time
to make a change, however that quantity is ineffective by itself. You do not know, is
this a great factor? Is it a nasty factor? Do we now have an issue?”.

A blended strategies strategy permits us to reap the benefits of the advantages of
each qualitative and quantitative metrics whereas getting a full perceive of
developer productiveness:

  1. Begin with qualitative knowledge to establish your high alternatives
  2. As soon as you already know what you need to enhance, use quantitative metrics to
    drill-in additional
  3. Observe your progress utilizing each qualitative and quantitative metrics

It’s only by combining as a lot knowledge as potential – each qualitative and
quantitative – that organizations can start to construct a full understanding of
developer productiveness.

In the long run, nevertheless, it’s necessary to recollect: organizations spend quite a bit
on extremely certified people that may observe and detect issues that log-based
metrics can’t. By tapping into the minds and voices of builders,
organizations can unlock insights beforehand seen as unattainable.