Measuring Developer Productiveness by way of People

Measuring Developer Productiveness by way of People
Measuring Developer Productiveness by way of People

Someplace, proper now, a expertise government tells their administrators: “we
want a option 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 evaluate their newly created
dashboards. Instantly, questions and doubts are raised. One chief says:
“Our lead time is 2 days which is ‘low performing’ based on these
benchmarks – however is there truly 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 among the greatest tech firms on this planet. It isn’t unusual
for measurement applications to fall brief when metrics like DORA fail to supply
the insights leaders had hoped for.

There’s, nevertheless, a greater method. An method that focuses on
capturing insights from builders themselves, somewhat than solely counting on
fundamental measures of velocity and output. We’ve helped many organizations make the
leap to this human-centered method. 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 method derived from our expertise
serving to many organizations on this journey. We start with a definition of
qualitative metrics and the right way to advocate for them. We comply with with sensible
steerage on the right way to seize, monitor, and make the most of this information.

At the moment, developer productiveness is a essential concern for companies amid
the backdrop of fiscal tightening and transformational applied sciences comparable 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 issues share is a reliance on measurement
to assist information choices and monitor 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 information
offered by people. It is a sensible definition – we haven’t discovered a
singular definition throughout the social sciences, and the choice
definitions we’ve seen have flaws that we focus on 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,” nevertheless, has no authoritative definition as famous within the
2019 journal paper What is Qualitative in
Qualitative Research
:

There are numerous 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 principle motive 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 often measured
by way of ordinal scales which are translated into numerical values and
scores – which once more, contradicts the definition.

One other argument we have now heard is that the output of sentiment evaluation
is quantitative as a result of the evaluation leads to numbers. Whereas we agree
that the information ensuing from sentiment evaluation is quantitative, primarily based on
our unique 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 in opposition to this phrase as a result of it harmfully and
incorrectly implies that information collected from people is weaker than “exhausting
metrics” collected from programs. We additionally discourage the time period “subjective
metrics” as a result of it misconstrues the truth that information collected from people
could be both goal or subjective – as we focus on 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 glad 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 steerage on the right way to gather and use
these measurements, however first we’ll present a real-world instance of this
method put to apply

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 crew, which is a part of their Product
Operations group.

Thansha Sadacharam, head of tech studying and insights, explains: “I
very strongly imagine, and I feel quite a lot of our engineers additionally actually
respect this, that engineers aren’t robots, they’re people. And simply
fundamental numbers does not drive the entire story. So for us, having
a extremely complete survey that helped us perceive that whole
developer expertise was actually vital.”

Every survey is shipped to
a random pattern of roughly half of their builders. With this method,
particular person builders solely have to take part in a single survey per yr,
minimizing the general time spent on filling out surveys whereas nonetheless
offering a statistically vital consultant set of information outcomes.
The Tech Enablement & Developer Expertise crew 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 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 information
for inspecting programs. Nonetheless, we can’t depend on this similar method 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 superb use of time or vitality. 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 explanation for opposition to qualitative information
are misconceptions which we handle under. Later on this article, we
define the distinct advantages of self-reported information comparable to its capability to
measure intangibles and floor essential context.

False impression: Qualitative information is barely subjective

Conventional office surveys usually give attention to the subjective
opinions and emotions of their workers. Thus many engineering leaders
intuitively imagine that surveys can solely gather subjective information from
builders.

As we describe within the following part, surveys also can 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 typically does your group deploy code to manufacturing or
    launch it to finish customers?

False impression: Qualitative information is unreliable

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

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

Whereas it’s true that survey information isn’t all the time 100% correct, we regularly
remind leaders that system metrics are sometimes imperfect too. For instance,
many organizations try and measure CI construct instances utilizing information aggregated
from their pipelines, solely to search out that it requires vital effort to
clear the information (e.g. excluding background jobs, accounting for parallel
jobs) to provide an correct consequence

The 2 sorts of qualitative metrics

There are two key sorts 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 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 almost all tech practitioners overlook behavioral measures
when serious 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 comparable to 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 under:

Lead time

For the first utility 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 1 month

Sooner or later to 1 week

Lower than someday

Lower than one hour

Deploy frequency

For the first utility 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 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 share

For the first utility or service you’re employed on, what
share of adjustments to manufacturing or releases to customers lead to
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 typically 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 1 month

Sooner or later to 1 week

Lower than someday

Lower than one hour

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

For instance, behavioral information may present you that your launch course of
is quick and environment friendly. However solely attitudinal information 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
price, they usually say “Properly, it appears such as you’re all good. There’s nothing
unsuitable with you.” You’d be stunned! You’d say, “Wait, I’m telling
you that one thing feels unsuitable.”

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 information – it doesn’t handle the principle goal
advantages that qualitative approaches can present.

There are three foremost advantages of qualitative metrics in the case of
measuring developer productiveness:

Qualitative metrics assist you to measure issues which are 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
features of builders’ work that must be understood with a purpose to enhance
productiveness: for instance, whether or not builders are in a position to keep within the stream
or work or simply navigate their codebases. Qualitative metrics allow you to
measure these intangibles which are in any other case tough or inconceivable to
measure.

An fascinating instance of that is technical debt. At Google, a examine 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 inconceivable as a result of the truth that
evaluation of technical debt depends on the comparability between the present
state of a system or codebase versus its imagined supreme state. In different
phrases, human judgment is crucial.

Qualitative metrics present lacking visibility throughout groups and
programs

Metrics from ticketing programs and pipelines give us visibility into
among the work that builders do. However this information alone can’t give us
the total story. Builders do quite a lot of work that’s not captured in tickets
or builds: for instance, designing key options, shaping the path of a
undertaking, or serving to a teammate get onboarded.

It’s inconceivable to realize visibility into all these actions by way of
information from our programs alone. And even when we may theoretically gather
all the information by way of programs, there are further challenges to capturing
metrics by way of instrumentation.

One instance is the issue of normalizing metrics throughout completely different
crew workflows. For instance, for those who’re making an attempt to measure how lengthy it takes
for duties to go from begin to completion, you may attempt to get this information
out of your ticketing device. However particular person groups typically have completely different
workflows that make it tough to provide an correct metric. In
distinction, merely asking builders how lengthy duties usually take could be
a lot less complicated.

One other frequent problem is cross-system visibility. For instance, a
small startup can measure TTR (time to revive) utilizing simply a difficulty
tracker comparable to Jira. A big group, nevertheless, will seemingly have to
consolidate and cross-attribute information throughout planning programs and deployment
pipelines with a purpose to achieve end-to-end system visibility. This is usually a
yearlong effort, whereas capturing this information from builders can present a
baseline rapidly.

Qualitative metrics present context for quantitative information

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

One instance of that is code evaluate: a typical optimization is to attempt to
velocity up the code evaluate. This appears logical as ready for a code evaluate
may cause wasted time or undesirable context switching. We may measure the
time it takes for evaluations to be accomplished and incentivize groups to enhance
it. However this method could encourage destructive habits: reviewers speeding
by way of evaluations or builders not discovering the proper consultants to carry out
evaluations.

Code evaluations 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 somewhat than simply velocity – we discover that optimization
of code evaluate should guarantee good code high quality, mitigation of safety
dangers, constructing shared information throughout crew 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 crew 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 protected to ask questions and if
they’re collaborating with cross-functional friends.

The 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. In actual fact, there are
complete fields of examine associated to this, comparable to psychometrics and
industrial psychology. You will need to convey 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 objects must 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 just’re measuring one thing completely different.
  • In the event you change any wording, you could do rigorous statistical assessments.

In survey parlance, ”good surveys” means “legitimate and dependable” or
“demonstrating good psychometric properties.” Validity is the diploma to
which a survey merchandise truly measures the assemble you want 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 offered a survey query, a collection of psychological
steps happen with a purpose to arrive at a response. The mannequin under is from
the seminal 2012 guide, The Psychology of Survey
Response
:

Parts of the Response Course of
Element Particular Processes
Comprehension

Attend to questions and directions

Characterize logical type of query

Determine query focus (data sought)

Hyperlink key phrases to related ideas

Retrieval

Generate retrieval technique and cues

Retrieve particular, generic reminiscences

Fill in lacking particulars

Judgment

Assess completeness and relevance of reminiscences

Draw inferences primarily based on accessibility

Combine materials retrieved

Make estimate primarily based on partial retrieval

Response

Map Judgement onto response class

Edit response

Decomposing the survey response course of and inspecting every step
may help us refine our inputs to provide extra correct survey outcomes.
Creating good survey objects 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.
Further challenges embody participation charges, information evaluation, and realizing
the right way to act on information. Beneath are among the greatest practices we’ve
realized.

Section outcomes by crew and persona

A typical mistake made by organizational leaders is to give attention to companywide
outcomes as an alternative of information damaged down by crew and persona (e.g., position, tenure,
seniority). As beforehand described, developer expertise is extremely contextual
and might 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 throughout the firm, comparable to cellular builders.

Evaluate outcomes in opposition to benchmarks

Comparative evaluation may help contextualize information and assist drive motion. For
instance, developer sentiment towards code high quality generally skews destructive, making
it tough to establish true issues or gauge their magnitude. The extra
actionable information 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 information 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 shouldn’t be worthwhile. It’s subsequently
essential 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 crew rituals comparable to retrospectives.

Survey Template

Beneath are a easy set of survey questions for getting began. Load the questions
under 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 change 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’ve got collected responses, rating the a number of alternative questions
utilizing both imply or prime field scoring. Imply scores are calculated by
assigning every choice a price between 1 and 5 and taking the typical.
Prime field scores are calculated by the chances of responses that
select one of many prime two most favorable choices.

Make sure to evaluate open textual content responses which might include nice
data. In the event you’ve collected a lot of feedback, LLM instruments
comparable to ChatGPT could be helpful for extracting core themes and
strategies. If you’ve completed analyzing outcomes, remember to 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 adjustments (that’s, how lengthy does it take to go
from code dedicated to code efficiently working in
manufacturing)?

A couple of month

One week to 1 month

Sooner or later to 1 week

Lower than someday

Lower than one hour

How typically do you are feeling extremely productive in your
work?

By no means

Just a little of the time

Among the time

More often than not

All the time

Please price your settlement or disagreement with the next
statements:

My crew follows improvement greatest practices
I’ve sufficient time for deep work.
I’m glad with the quantity of automated take a look at protection in
my undertaking.
It is easy for me to deploy to manufacturing.
I am glad with the standard of our CI/CD tooling.
My crew’s codebase is straightforward for me to contribute to.
The quantity of technical debt on my crew is suitable primarily based on our targets.
Specs are repeatedly revisited and reprioritized based on 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, however, present
distinct benefits as properly:

  • Precision. People can let you know whether or not their CI/CD builds are typically
    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. Sometimes, the frequency at which a company can survey
    their builders is at most a couple of times per quarter. So as to gather extra
    frequent or steady metrics, organizations should collect information
    systematically.

Finally, it’s by way of the mixture of qualitative and quantitative metrics – a mixed-methods method
that organizations can achieve 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 after they begin with qualitative
metrics to ascertain 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 method to be efficient as a result of qualitative
metrics present a holistic view and context, offering extensive understanding of
potential alternatives. Quantitative metrics, however, are
usually solely obtainable for a narrower set of the software program supply
course of.

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

A blended strategies method permits us to benefit from the advantages of
each qualitative and quantitative metrics whereas getting a full perceive of
developer productiveness:

  1. Begin with qualitative information to establish your prime 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 is just by combining as a lot information as doable – each qualitative and
quantitative – that organizations can start to construct a full understanding of
developer productiveness.

Ultimately, nevertheless, it’s vital 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 inconceivable.