May 18, 2024

Someplace, proper now, a know-how government 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 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 unsure if this spells a chance 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 the earth. It’s not unusual
for measurement applications to fall quick when metrics like DORA fail to supply
the insights leaders had hoped for.

There may be, nonetheless, a greater strategy. An strategy that focuses on
capturing insights from builders themselves, slightly than solely counting on
primary measures of pace 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 advocate for them. We observe with sensible
steerage on seize, monitor, and make the most of this information.

As we speak, developer productiveness is a crucial concern for companies amid
the backdrop of fiscal tightening and transformational applied sciences corresponding 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 selections and monitor progress. And for this, qualitative
measurement is vital.

Word: 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 way in which it may be misinterpreted by
builders. We suggest that organizations use the time period “developer
expertise,” which has extra optimistic connotations for builders.

What’s a qualitative metric?

We outline a qualitative metric as a measurement comprised of information
supplied by people. This can be 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,” 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 function 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” contains the time period metric, which suggests that the output is a
amount (i.e., a measurement). Second, high quality is often measured
by ordinal scales which can 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 ends in numbers. Whereas we agree
that the information ensuing from sentiment evaluation is quantitative, based mostly on
our unique definition that is nonetheless a qualitative metric (i.e., a amount
produced qualitatively) until one have been to take the place that
“qualitative metric” is altogether an oxymoron.

Other than 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 information collected from people is weaker than “exhausting
metrics” collected from techniques. We additionally discourage the time period “subjective
metrics” as a result of it misconstrues the truth that information collected from people
may be both goal or subjective – as we focus on within the subsequent
part.

Qualitative metrics: Measurements derived from people
Sort 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 gather and use
these measurements, however first we’ll present a real-world instance of this
strategy put to observe

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

Thansha Sadacharam, head of tech studying and insights, explains: “I
very strongly imagine, and I believe lots of our engineers additionally actually
admire 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 essential.”

Every survey is shipped to
a random pattern of roughly half of their builders. With this strategy,
particular person builders solely have to take part in a single survey per 12 months,
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 workforce can be chargeable 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 techniques. Nonetheless, we can’t depend on this similar strategy for
measuring individuals.

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 use of time or power. 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 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 corresponding to its means to
measure intangibles and floor crucial context.

False impression: Qualitative information is just subjective

Conventional office surveys sometimes concentrate on the subjective
opinions and emotions of their staff. 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 info 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
    operating 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, nonetheless, produce
correct and dependable information (we offer steerage on how to do that later in
the article).

Some organizations have issues that folks might lie in surveys. Which
can occur in conditions the place there may be worry round how the information shall 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 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 occasions 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 kinds of qualitative metrics

There are two key kinds 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 the majority tech practitioners overlook behavioral measures
when occupied with 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 corresponding to lead time for
modifications, deployment frequency, and alter fail charge. Unbeknownst to many,
DORA’s benchmarks are captured utilizing qualitative strategies with the survey
gadgets proven under:

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 operating 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 in the future

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 monthly and as soon as each six months

Between as soon as per week and as soon as monthly

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 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 in the future

Lower than one hour

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

For instance, behavioral information would possibly present you that your launch course of
is quick and environment friendly. However solely attitudinal information might let you know whether or not it
is clean and painless, which has essential implications for developer
burnout and retention.

To make use of a non-tech analogy: think about you feel sick and go to a
physician. The physician takes your blood strain, your temperature, your coronary heart
charge, they usually say “Properly, it appears to be like such as you’re all good. There’s nothing
incorrect with you.” You’d be shocked! You’d say, “Wait, I’m telling
you that one thing feels incorrect.”

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 primary advantages of qualitative metrics in relation to
measuring developer productiveness:

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

System metrics like lead time and deployment quantity seize what’s
taking place in our pipelines or ticketing techniques. However there are a lot of extra
facets of builders’ work that should be understood in an effort to enhance
productiveness: for instance, whether or not builders are in a position to keep within the move
or work or simply navigate their codebases. Qualitative metrics allow you to
measure these intangibles which can be in any other case tough or unattainable to
measure.

An attention-grabbing instance of that is technical debt. At Google, a research to
determine 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 might exist an undiscovered goal metric for technical
debt, one can suppose that this can be unattainable 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
techniques

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

It’s unattainable to achieve visibility into all these actions by
information from our techniques alone. And even when we might theoretically gather
all the information by techniques, there are further challenges to capturing
metrics by instrumentation.

One instance is the issue of normalizing metrics throughout totally different
workforce workflows. For instance, when you’re attempting to measure how lengthy it takes
for duties to go from begin to completion, you would possibly attempt to get this information
out of your ticketing software. However particular person groups typically have totally different
workflows that make it tough to provide an correct metric. In
distinction, merely asking builders how lengthy duties sometimes take may 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 corresponding to Jira. A big group, nonetheless, will doubtless have to
consolidate and cross-attribute information throughout planning techniques and deployment
pipelines in an effort 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 danger, nonetheless, that the
full story isn’t being advised with out richer information and that this will likely lead us
into specializing in the incorrect factor.

One instance of that is code overview: a typical optimization is to attempt to
pace 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 evaluations to be accomplished and incentivize groups to enhance
it. However this strategy might encourage detrimental conduct: reviewers speeding
by evaluations or builders not discovering the precise consultants to carry out
evaluations.

Code evaluations exist for an essential function: to make sure top quality
software program is delivered. If we do a extra holistic evaluation – specializing in the
outcomes of the method slightly than simply pace – we discover that optimization
of code overview should guarantee good code high quality, mitigation of safety
dangers, constructing shared data throughout workforce members, in addition to guaranteeing
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 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
essential outcomes e.g. whether or not we’re absolutely 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.

Learn how to seize qualitative metrics

Many tech practitioners don’t understand how tough it’s to write down good
survey questions and design good survey devices. The truth is, there are
complete fields of research associated to this, corresponding to psychometrics and
industrial psychology. It is very important carry or construct experience right here
when attainable.

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

  • Survey gadgets should be rigorously 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 totally different.
  • In case you change any wording, you have 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 truly 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 offered a survey query, a sequence of psychological
steps happen in an effort to arrive at a response. The mannequin under is from
the seminal 2012 guide, The Psychology of Survey
Response
:

Elements of the Response Course of
Part Particular Processes
Comprehension

Attend to questions and directions

Signify logical type of query

Determine query focus (info 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 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.
Creating good survey gadgets requires rigorous design, testing, and
evaluation – similar to the method of designing software program!

However good survey design is only one facet of operating profitable surveys.
Extra challenges embody participation charges, information evaluation, and understanding
act on information. Beneath are among the finest practices we’ve
discovered.

Section outcomes by workforce and persona

A standard mistake made by organizational leaders is to concentrate on companywide
outcomes as a substitute of information damaged down by workforce and persona (e.g., position, tenure,
seniority). As beforehand described, developer expertise is very 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 essential
populations throughout the firm, corresponding to cellular builders.

Evaluate outcomes towards benchmarks

Comparative evaluation might help contextualize information and assist drive motion. For
instance, developer sentiment towards code high quality generally skews detrimental, making
it tough to determine 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 business 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
crucial that leaders and groups observe 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 can be built-in into
common workforce rituals corresponding to retrospectives.

Survey Template

Beneath are a easy set of survey questions for getting began. Load the questions
under into your most well-liked 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 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 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 typical.
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
corresponding to ChatGPT may be helpful for extracting core themes and
strategies. Whenever you’ve completed analyzing outcomes, make 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 operating in
manufacturing)?

Multiple month

One week to at least one month

Someday to at least one week

Lower than in the future

Lower than one hour

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

By no means

A bit of the time

A few of the time

More often than not

The entire time

Please charge your settlement or disagreement with the next
statements:

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 simple for me to deploy to manufacturing.
I am glad with the standard of our CI/CD tooling.
My workforce’s codebase is straightforward for me to contribute to.
The quantity of technical debt on my workforce is suitable based mostly on our targets.
Specs are repeatedly revisited and reprioritized in line with person alerts.

Please share any further suggestions on how your developer expertise might 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 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 occasions 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. So as to gather extra
    frequent or steady metrics, organizations should collect information
    systematically.

Finally, it’s by the mix 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, observe 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 extensive understanding of
potential alternatives. Quantitative metrics, however, are
sometimes solely accessible 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 when you solely take a look at logs information it does not actually let you know whether or not
one thing is nice or unhealthy. 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 factor? Is it a foul factor? Do now we have an issue?”.

A combined strategies strategy 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 determine 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’s only by combining as a lot information as attainable – each qualitative and
quantitative – that organizations can start to construct a full understanding of
developer productiveness.

Ultimately, nonetheless, it’s essential 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 unattainable.