Someplace, proper now, a expertise government tells their administrators: “we
want a strategy 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’ 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 usually than
others. However I’m unsure 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 largest 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, nonetheless, a greater strategy. An strategy that focuses on
capturing insights from builders themselves, relatively than solely counting on
fundamental 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
gives.
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 tips on how to advocate for them. We comply with with sensible
steering on tips on how to seize, monitor, and make the most of this knowledge.
Right now, developer productiveness is a crucial concern for companies amid
the backdrop of fiscal tightening and transformational applied sciences resembling
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 selections and monitor progress. And for this, qualitative
measurement is vital.
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 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
offered by people. This can be 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,” nonetheless, has no authoritative definition as famous within the
2019 journal paper What’s Qualitative in
Qualitative Analysis:
There are a lot of definitions of qualitative analysis, but when we search for
a definition that addresses its distinctive characteristic of being
“qualitative,” the literature throughout the broad subject 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 suggests that the output is a
amount (i.e., a measurement). Second, high quality is often measured
by way of ordinal scales which can be translated into numerical values and
scores – which once more, contradicts the definition.
One other argument we’ve got 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 unique definition that is nonetheless a qualitative metric (i.e., a amount
produced qualitatively) until one had been to take the place that
“qualitative metric” is altogether an oxymoron.
Apart from the issue of defining what a qualitative metric is, we’ve
additionally encountered problematic colloquialisms. One instance is the time period “tender
metric”. We warning towards this phrase as a result of it harmfully and
incorrectly implies that knowledge collected from people is weaker than “arduous
metrics” collected from techniques. We additionally discourage the time period “subjective
metrics” as a result of it misconstrues the truth that knowledge collected from people
will be both goal or subjective – as we focus on within the subsequent
part.
| Sort | Definition | Instance |
|---|---|---|
| Attitudinal metrics | Subjective emotions, opinions, or attitudes towards a particular topic. | How happy are you along with your IDE, on a scale of 1–10? |
| Behavioral metrics | Goal 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 steering on tips on how to gather and use
these measurements, however first we’ll present a real-world instance of this
strategy 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 workforce, 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 a number of our engineers additionally actually
respect 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 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 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
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 techniques. Nevertheless, we can not depend on this identical strategy for
measuring individuals.
Keep away from pitting qualitative and quantitative metrics towards one another.
We’ve seen some organizations get into an inside “battle of the
metrics” which isn’t a superb 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 behind opposition to qualitative knowledge
are misconceptions which we tackle under. Later on this article, we
define the distinct advantages of self-reported knowledge resembling its skill to
measure intangibles and floor crucial context.
False impression: Qualitative knowledge is barely subjective
Conventional office surveys usually deal with the subjective
opinions and emotions of their staff. Thus many engineering leaders
intuitively consider that surveys can solely gather subjective knowledge from
builders.
As we describe within the following part, surveys can even seize
goal details about information or occasions. Google’s DevOps Analysis and
Evaluation (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. Properly 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 worry round how the info can 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 recreation the system.
Whereas it’s true that survey knowledge isn’t at all times 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 search out that it requires vital effort to
clear the info (e.g. excluding background jobs, accounting for parallel
jobs) to provide an correct consequence
The 2 forms of qualitative metrics
There are two key forms of qualitative metrics:
- Attitudinal metrics seize subjective emotions, opinions, or
attitudes towards a particular 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?”. - Behavioral metrics seize goal information 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 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 resembling 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 working 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 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 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 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
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 robust 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 might 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 feel sick and go to a
physician. The physician takes your blood stress, your temperature, your coronary heart
charge, they usually say “Properly, it seems to be such as you’re all good. There’s nothing
improper with you.” You’ll be greatly surprised! You’d say, “Wait, I’m telling
you that one thing feels improper.”
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 tackle the principle goal
advantages that qualitative approaches can present.
There are three major advantages of qualitative metrics on the subject of
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
occurring in our pipelines or ticketing techniques. However there are lots of extra
points of builders’ work that have to be understood as a way to enhance
productiveness: for instance, whether or not builders are capable of keep within the circulate
or work or simply navigate their codebases. Qualitative metrics allow you to
measure these intangibles which can be in any other case tough or inconceivable 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,
take heed to this interview).
Whereas there could exist an undiscovered goal metric for technical
debt, one can suppose that this can be inconceivable 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 preferrred 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 knowledge alone can not give us
the total story. Builders do a number 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 achieve visibility into all these actions by way of
knowledge from our techniques alone. And even when we might theoretically gather
all the info by way of techniques, there are extra challenges to capturing
metrics by way of instrumentation.
One instance is the problem of normalizing metrics throughout totally different
workforce workflows. For instance, should you’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 usually have totally different
workflows that make it tough to provide an correct metric. In
distinction, merely asking builders how lengthy duties usually take will 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 resembling Jira. A big group, nonetheless, will probably have to
consolidate and cross-attribute knowledge throughout planning techniques and deployment
pipelines as a way to achieve end-to-end system visibility. This generally is 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 threat, nonetheless, that the
full story isn’t being advised with out richer knowledge and that this may occasionally lead us
into specializing in the improper 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
could 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 could encourage destructive habits: reviewers dashing
by way of evaluations or builders not discovering the precise 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 relatively than simply pace – we discover that optimization
of code overview should guarantee good code high quality, mitigation of safety
dangers, constructing shared information throughout workforce members, in addition to guaranteeing
that our coworkers aren’t caught ready. Qualitative measures may also help us
assess whether or not these outcomes are being met.
One other instance is developer onboarding processes. Software program growth
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
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.