Measuring Developer Productiveness by way of People

Someplace, proper now, a know-how govt tells their administrators: “we
want a method 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 line 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 usually 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 greatest tech corporations on the earth. It isn’t unusual
for measurement packages to fall brief when metrics like DORA fail to offer
the insights leaders had hoped for.

There’s, nonetheless, a greater strategy. An strategy that focuses on
capturing insights from builders themselves, moderately 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
offers.

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 find out how to advocate for them. We comply with with sensible
steering on find out how to seize, monitor, 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 equivalent to
AI. As well as, developer expertise and platform engineering are garnering
elevated consideration as enterprises look past Agile and DevOps
transformation. What all these considerations share is a reliance on measurement
to assist information choices and monitor progress. And for this, qualitative
measurement is vital.

Be aware: after 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 talk about later on this
part.

Determine 1: Qualitative metrics are measurements derived from people

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

There are various definitions of qualitative analysis, but when we search for
a definition that addresses its distinctive characteristic of being
“qualitative,” the literature throughout the broad discipline of social science is
meager. The primary 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 not 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 usually measured
by means of ordinal scales which might be translated into numerical values and
scores – which once more, contradicts the definition.

One other argument now we have heard is that the output of sentiment evaluation
is quantitative as a result of the evaluation leads to numbers. Whereas we agree
that the info ensuing from sentiment evaluation is quantitative, based mostly on
our authentic definition that is nonetheless a qualitative metric (i.e., a amount
produced qualitatively) except one had 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 “delicate
metric”. We warning towards 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
might be both goal or subjective – as we talk about within the subsequent
part.

Qualitative metrics: Measurements derived from people
Kind Definition Instance
Attitudinal metrics Subjective emotions, opinions, or attitudes towards a selected topic. How happy are you along with your IDE, on a scale of 1–10?
Behavioral metrics Goal details 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 steering on find out how to acquire and use
these measurements, however first we’ll present a real-world instance of this
strategy put to apply

Peloton is an American know-how 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 group, 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
respect this, that engineers aren’t robots, they’re people. And simply
primary numbers does not drive the entire story. So for us, having
a very complete survey that helped us perceive that total
developer expertise was actually vital.”

Every survey is distributed 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 yr,
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 group can be answerable 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
interview

with Thansha Sadacharam.

Advocating for qualitative metrics

Executives are sometimes skeptical concerning 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. Nonetheless, we can not depend on this identical strategy for
measuring folks.

Keep away from pitting qualitative and quantitative metrics towards one another.

We’ve seen some organizations get into an inner “battle of the
metrics” which isn’t a very good use of time or vitality. Our recommendation for
champions is to keep away from pitting qualitative and quantitative metrics towards
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 behind opposition to qualitative knowledge
are misconceptions which we handle beneath. Later on this article, we
define the distinct advantages of self-reported knowledge equivalent to its potential to
measure intangibles and floor important context.

False impression: Qualitative knowledge is simply 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 acquire subjective knowledge from
builders.

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

Some examples of goal survey questions:

  • How lengthy does it take to go from code dedicated to code efficiently
    working in manufacturing?
  • How usually 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 provide
dependable or legitimate measures. Effectively designed surveys, nonetheless, produce
correct and dependable knowledge (we offer steering on how to do that later in
the article).

Some organizations have considerations that individuals could lie in surveys. Which
can occur in conditions the place there’s concern round how the info will likely be
used. In our expertise, when surveys are deployed as a software 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 all the time 100% correct, we frequently
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 search out that it requires important effort to
clear the info (e.g. excluding background jobs, accounting for parallel
jobs) to provide an correct outcome

The 2 varieties of qualitative metrics

There are two key varieties 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 happy are
    you along with your IDE, on a scale of 1-10?”.
  2. Behavioral metrics seize goal details 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 the majority tech practitioners overlook behavioral measures
when eager 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 equivalent to lead time for
modifications, deployment frequency, and alter fail fee. Unbeknownst to many,
DORA’s benchmarks are captured utilizing qualitative strategies with the survey
gadgets proven beneath:

Lead time

For the first utility or service you’re employed on,
what’s your lead time for modifications (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 at some point

Lower than one hour

Deploy frequency

For the first utility or service you
work on, how usually 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 30 days and as soon as each six months

Between as soon as per week and as soon as per 30 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 utility or service you’re employed on, what
proportion of modifications 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)?

0–15%

16–30%

31–45%

46–60%

61–75%

76–100%

Time to revive

For the first utility 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
impairment)?

Greater than six months

One to 6 months

One week to at least one month

Someday to at least one week

Lower than at some point

Lower than one hour

We’ve discovered that the flexibility to gather attitudinal and behavioral knowledge
on the identical time is a strong good thing about qualitative measurement.

For instance, behavioral knowledge would possibly present you that your launch course of
is quick and environment friendly. However solely attitudinal knowledge may let you know whether or not it
is clean and painless, which has vital 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
fee, they usually say “Effectively, it seems to be such as you’re all good. There’s nothing
flawed with you.” You’ll be stunned! You’d say, “Wait, I’m telling
you that one thing feels flawed.”

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 handle the principle goal
advantages that qualitative approaches can present.

There are three most important advantages of qualitative metrics on the subject of
measuring developer productiveness:

Qualitative metrics help you measure issues which might be in any other case
unmeasurable

System metrics like lead time and deployment quantity seize what’s
occurring in our pipelines or ticketing programs. However there are lots of extra
facets of builders’ work that must be understood to be able to enhance
productiveness: for instance, whether or not builders are capable of keep within the move
or work or simply navigate their codebases. Qualitative metrics allow you to
measure these intangibles which might be in any other case tough or not possible to
measure.

An fascinating instance of that is technical debt. At Google, a research to
establish metrics for technical debt included an evaluation of 117 metrics
that had been proposed as potential indicators. To the frustration of
Google researchers, no single metric or mixture of metrics had 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 not possible as a consequence 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 important.

Qualitative metrics present lacking visibility throughout groups and
programs

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 course of a
venture, or serving to a teammate get onboarded.

It’s not possible to realize visibility into all these actions by means of
knowledge from our programs alone. And even when we may theoretically acquire
all the info by means of programs, there are further challenges to capturing
metrics by means of instrumentation.

One instance is the issue of normalizing metrics throughout completely different
group workflows. For instance, when you’re making an attempt to measure how lengthy it takes
for duties to go from begin to completion, you would possibly attempt to get this knowledge
out of your ticketing software. However particular person groups usually have completely different
workflows that make it tough to provide an correct metric. In
distinction, merely asking builders how lengthy duties sometimes take might be
a lot less complicated.

One other widespread problem is cross-system visibility. For instance, a
small startup can measure TTR (time to revive) utilizing simply a difficulty
tracker equivalent to Jira. A big group, nonetheless, will doubtless must
consolidate and cross-attribute knowledge throughout planning programs and deployment
pipelines to be able to achieve 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, nonetheless, that the
full story isn’t being instructed with out richer knowledge and that this will likely lead us
into specializing in the flawed 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 may measure the
time it takes for critiques to be accomplished and incentivize groups to enhance
it. However this strategy could encourage detrimental conduct: reviewers dashing
by means of critiques or builders not discovering the precise consultants to carry out
critiques.

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

One other instance is developer onboarding processes. Software program improvement
is a group 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
vital 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.

The best way to seize qualitative metrics

Many tech practitioners don’t notice how tough it’s to put in writing good
survey questions and design good survey devices. Actually, there are
entire fields of research associated to this, equivalent to psychometrics and
industrial psychology. You will need to deliver or construct experience right here
when doable.

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

  • Survey gadgets must be fastidiously worded and each query ought to solely ask
    one factor.
  • If you wish to examine outcomes between surveys, watch out about altering
    the wording of questions such that you just’re measuring one thing completely different.
  • Should you change any wording, you should 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 collection of psychological
steps happen to be able to arrive at a response. The mannequin beneath is from
the seminal 2012 guide, The Psychology of Survey
Response
:

Elements of the Response Course of
Element Particular Processes
Comprehension

Attend to questions and directions

Signify logical type of query

Establish query focus (data sought)

Hyperlink key phrases to related ideas

Retrieval

Generate retrieval technique and cues

Retrieve particular, generic recollections

Fill in lacking particulars

Judgment

Assess completeness and relevance of recollections

Draw inferences based mostly on accessibility

Combine materials retrieved

Make estimate based mostly on partial retrieval

Response

Map Judgement onto response class

Edit response

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

However good survey design is only one side of working profitable surveys.
Extra challenges embrace participation charges, knowledge evaluation, and understanding
find out how to act on knowledge. Beneath are a few of the greatest practices we’ve
discovered.

Phase outcomes by group and persona

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

Evaluate outcomes towards benchmarks

Comparative evaluation might help contextualize knowledge and assist drive motion. For
instance, developer sentiment towards code high quality generally skews detrimental, making
it tough to establish true issues or gauge their magnitude. The extra
actionable knowledge level is: “are our builders extra annoyed 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 applicable

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 is just not 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 might be built-in into
common group rituals equivalent to retrospectives.

Survey Template

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

The template is deliberately easy, however surveys usually 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 possibility a worth between 1 and 5 and taking the common.
High field scores are calculated by the chances of responses that
select one of many high two most favorable choices.

Be sure you overview open textual content responses which might comprise nice
data. Should you’ve collected a lot of feedback, LLM instruments
equivalent to ChatGPT might be helpful for extracting core themes and
recommendations. Whenever you’ve completed analyzing outcomes, be sure you share
your findings with respondents so their time filling out the survey
feels worthwhile.

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

Very tough

Considerably tough

Neither straightforward nor tough

Considerably straightforward

Very straightforward

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

Multiple month

One week to at least one month

Someday to at least one week

Lower than at some point

Lower than one hour

How usually do you’re feeling extremely productive in your
work?

By no means

Slightly of the time

A number of the time

More often than not

All the time

Please fee your settlement or disagreement with the next
statements:

My group follows improvement greatest practices
I’ve sufficient time for deep work.
I’m happy with the quantity of automated take a look at protection in
my venture.
It is simple for me to deploy to manufacturing.
I am happy with the standard of our CI/CD tooling.
My group’s codebase is straightforward for me to contribute to.
The quantity of technical debt on my group is acceptable based mostly on our targets.
Specs are constantly revisited and reprioritized in line with person alerts.

Please share any further suggestions on how your developer expertise may very well 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, then again, present
distinct benefits as effectively:

  • Precision. People can let you know 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 not report on construct instances right down to millisecond precision. Quantitative
    metrics are wanted when a excessive diploma of precision is required in our
    measurements.
  • Continuity. Usually, the frequency at which a corporation can survey
    their builders is at most a few times per quarter. In an effort to acquire extra
    frequent or steady metrics, organizations should collect knowledge
    systematically.

In the end, it’s by means of the mixture of qualitative and quantitative metrics – a mixed-methods strategy
that organizations can achieve most visibility into the productiveness and
expertise of builders. So how do you employ 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 vast understanding of
potential alternatives. Quantitative metrics, then again, 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 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 when you solely have a look at logs knowledge it does not actually let you know whether or not
one thing is nice or dangerous. For instance, now we 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 very good factor? Is it a nasty factor? Do now we have an issue?”.

A combined strategies strategy permits us to make the most 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 understand what you need to enhance, use quantitative metrics to
    drill-in additional
  3. Monitor your progress utilizing each qualitative and quantitative metrics

It is just by combining as a lot knowledge as doable – each qualitative and
quantitative – that organizations can start to construct a full understanding of
developer productiveness.

In the long run, nonetheless, it’s vital to recollect: organizations spend lots
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 not possible.