Measuring Developer Productiveness through People


Someplace, proper now, a expertise govt tells their administrators: “we
want a solution 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 evaluation 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 a number of the largest tech firms on this planet. It isn’t unusual
for measurement applications to fall brief when metrics like DORA fail to offer
the insights leaders had hoped for.

There may be, nonetheless, a greater method. An method that focuses on
capturing insights from builders themselves, reasonably than solely counting on
primary measures of pace 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

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 advocate for them. We comply with with sensible
steerage on seize, observe, and make the most of this knowledge.

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 considerations share is a reliance on measurement
to assist information choices and observe progress. And for this, qualitative
measurement is essential.

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 advocate 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 knowledge
supplied 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 talk about later on this

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’s Qualitative in
Qualitative Analysis

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 purpose 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 means that the output is a
amount (i.e., a measurement). Second, high quality is often measured
by way of ordinal scales which might be 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 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 knowledge collected from people is weaker than “laborious
metrics” collected from methods. We additionally discourage the time period “subjective
metrics” as a result of it misconstrues the truth that knowledge collected from people
may be both goal or subjective – as we talk about within the subsequent

Qualitative metrics: Measurements derived from people


Attitudinal metricsSubjective emotions, opinions, or attitudes towards a particular topic.How glad are you along with your IDE, on a scale of 1–10?

Behavioral metricsObjective information 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
method 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 imagine, and I believe a variety of our engineers additionally actually
admire this, that engineers aren’t robots, they’re people. And simply
primary numbers would not drive the entire story. So for us, having
a extremely complete survey that helped us perceive that complete
developer expertise was actually vital.”

Every survey is distributed 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 knowledge 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, take heed to this

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 methods. Nevertheless, we can’t depend on this identical method 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 explanation for opposition to qualitative knowledge
are misconceptions which we deal with under. Later on this article, we
define the distinct advantages of self-reported knowledge comparable to its skill to
measure intangibles and floor essential context.

False impression: Qualitative knowledge is simply subjective

Conventional office surveys sometimes deal with the subjective
opinions and emotions of their workers. Thus many engineering leaders
intuitively imagine that surveys can solely gather subjective knowledge from

As we describe within the following part, surveys also can seize
goal details about information or occasions. Google’s DevOps Analysis and
Evaluation (DORA)
program is a superb 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. 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 knowledge (we offer steerage 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 may be worry round how the information 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 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 occasions utilizing knowledge aggregated
from their pipelines, solely to seek out that it requires vital effort to
clear the information (e.g. excluding background jobs, accounting for parallel
jobs) to provide an correct end result

We’re releasing this text in installments. Future installments will
describe the 2 varieties of qualitative metrics, clarify their advantages,
and go into element on seize them.

To search out out once we publish the subsequent installment subscribe to the
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