April 19, 2024

Someplace, proper now, a expertise govt tells their administrators: “we
want a approach 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 the planet. It isn’t unusual
for measurement packages to fall quick 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, reasonably 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
supplies.

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 methods to advocate for them. We comply with with sensible
steering on methods to seize, monitor, and make the most of this knowledge.

Right this moment, developer productiveness is a important 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 selections and monitor progress. And for this, qualitative
measurement is essential.

Observe: 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 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 knowledge
offered by people. It is a sensible definition – we haven’t discovered a
singular definition inside the social sciences, and the choice
definitions we’ve seen have flaws that we focus on later on this
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 function of being
“qualitative,” the literature throughout the broad discipline of social science is
meager. The principle cause behind this text lies within the paradox, which, to
put it bluntly, is that researchers act as in the event that they know what it’s, however
they can 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 usually measured
via ordinal scales which can be translated into numerical values and
scores – which once more, contradicts the definition.

One other argument we’ve 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) except one have been to take the place that
“qualitative metric” is altogether an oxymoron.

Except for the issue of defining what a qualitative metric is, we’ve
additionally encountered problematic colloquialisms. One instance is the time period “smooth
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
might 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 along 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 steering on methods to gather and use
these measurements, however first we’ll present a real-world instance of this
method put to follow

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 group, 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 lots of our engineers additionally actually
recognize this, that engineers aren’t robots, they’re people. And simply
taking a look at fundamental numbers would not drive the entire story. So for us, having
a very complete survey that helped us perceive that whole
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 must 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 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 methods. Nonetheless, we can’t depend on this identical method 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 a very good 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 reason for opposition to qualitative knowledge
are misconceptions which we handle beneath. Later on this article, we
define the distinct advantages of self-reported knowledge comparable to its skill to
measure intangibles and floor important context.

False impression: Qualitative knowledge is just subjective

Conventional office surveys usually deal with the subjective
opinions and emotions of their staff. Thus many engineering leaders
intuitively imagine that surveys can solely gather subjective knowledge 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 wonderful 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 knowledge is unreliable

One problem of surveys is that folks with all method of backgrounds
write survey questions with no particular coaching. Because of this, many
office surveys don’t meet the minimal requirements wanted to provide
dependable or legitimate measures. Properly designed surveys, nevertheless, produce
correct and dependable knowledge (we offer steering 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 might be
used. In our expertise, when surveys are deployed as a instrument to assist
perceive and enhance bottlenecks affecting builders, there is no such thing as a
incentive for respondents to lie or sport 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 and measure CI construct instances 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 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 along 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 could 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 fascinated 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
modifications, deployment frequency, and alter fail charge. Unbeknownst to many,
DORA’s benchmarks are captured utilizing qualitative strategies with the survey
objects proven beneath:

Lead time

For the first software or service you’re employed on,
what’s your lead time for 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

Sooner or later to at least one week

Lower than at some point

Lower than one hour

Deploy frequency

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

Fewer than as soon as per six months

Between as soon as per 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 software or service you’re employed on, what
proportion of modifications 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 software 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 at least one month

Sooner or later 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 may present you that your launch course of
is quick and environment friendly. However solely attitudinal knowledge may inform you 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
charge, and so they say “Properly, it appears such as you’re all good. There’s nothing
unsuitable with you.” You’ll be bowled over! 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 knowledge – it doesn’t handle the principle goal
advantages that qualitative approaches can present.

There are three most important advantages of qualitative metrics in relation to
measuring developer productiveness:

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

System metrics like lead time and deployment quantity seize what’s
occurring in our pipelines or ticketing methods. However there are lots of extra
features 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 stream
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 examine 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 attributable to 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
methods

Metrics from ticketing methods and pipelines give us visibility into
among the work that builders do. However this knowledge alone can’t give us
the total story. Builders do lots 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 unattainable to realize visibility into all these actions via
knowledge from our methods alone. And even when we may theoretically gather
all the information via methods, there are extra challenges to capturing
metrics via instrumentation.

One instance is the problem of normalizing metrics throughout totally different
group workflows. For instance, if you happen to’re attempting to measure how lengthy it takes
for duties to go from begin to completion, you may attempt to get this knowledge
out of your ticketing instrument. 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 usually take might be
a lot easier.

One other frequent problem is cross-system visibility. For instance, a
small startup can measure TTR (time to revive) utilizing simply a problem
tracker comparable to Jira. A big group, nevertheless, will doubtless must
consolidate and cross-attribute knowledge throughout planning methods and deployment
pipelines to be able to acquire end-to-end system visibility. This generally is a
yearlong effort, whereas capturing this knowledge from builders can present a
baseline shortly.

Qualitative metrics present context for quantitative knowledge

As technologists, it’s simple 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 knowledge and that this will 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
could 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 might encourage unfavorable conduct: reviewers speeding
via evaluations or builders not discovering the best specialists to carry out
evaluations.

Code evaluations exist for an vital objective: to make sure prime quality
software program is delivered. If we do a extra holistic evaluation – specializing in the
outcomes of the method reasonably than simply velocity – we discover that optimization
of code evaluate 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 will help us
assess whether or not these outcomes are being met.

One other instance is developer onboarding processes. Software program growth
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 protected to ask questions and if
they’re collaborating with cross-functional friends.

We’re releasing this text in installments. The subsequent installment
will go into element on methods to seize these sorts of metrics.

To seek out out after we publish the subsequent installment subscribe to the
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