Someplace, proper now, a know-how government tells their administrators: “we
want a technique 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’ in response to 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 chance for enchancment.”
If this story arc is acquainted to you, don’t fear – it is acquainted to
most, together with a few of the largest tech corporations on this planet. It isn’t unusual
for measurement packages to fall quick when metrics like DORA fail to offer
the insights leaders had hoped for.
There may be, nevertheless, a greater method. An method that focuses on
capturing insights from builders themselves, relatively 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.
As we speak, developer productiveness is a vital 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 choices and monitor progress. And for this, qualitative
measurement is vital.
Notice: 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 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 talk about 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’s Qualitative in
Qualitative Analysis:
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 subject of social science is
meager. The primary 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” consists of the time period metric, which suggests that the output is a
amount (i.e., a measurement). Second, high quality is usually measured
by ordinal scales which are translated into numerical values and
scores – which once more, contradicts the definition.
One other argument we now 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, primarily based 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.
Apart from 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
could be both goal or subjective – as we talk about within the subsequent
part.
| Kind | 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 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 acquire and use
these measurements, however first we’ll present a real-world instance of this
method 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 consider, and I feel a number of our engineers additionally actually
recognize this, that engineers aren’t robots, they’re people. And simply
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 necessary.”
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 important consultant set of information outcomes.
The Tech Enablement & Developer Expertise workforce can be liable for
analyzing and sharing the findings from their surveys with leaders throughout
the group.
For extra on Peloton’s developer expertise survey, hearken 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. 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 superb 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 handle beneath. Later on this article, we
define the distinct advantages of self-reported knowledge resembling its capability to
measure intangibles and floor vital context.
False impression: Qualitative knowledge is barely subjective
Conventional office surveys usually concentrate on the subjective
opinions and emotions of their staff. Thus many engineering leaders
intuitively consider that surveys can solely acquire subjective knowledge from
builders.
As we describe within the following part, surveys may seize
goal details about info or occasions. Google’s DevOps Analysis and
Evaluation (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
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 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 supply
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 considerations that folks could lie in surveys. Which
can occur in conditions the place there’s worry round how the information 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 sport the system.
Whereas it’s true that survey knowledge 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 knowledge aggregated
from their pipelines, solely to seek out that it requires important effort to
clear the information (e.g. excluding background jobs, accounting for parallel
jobs) to supply an correct outcome
The 2 sorts of qualitative metrics
There are two key sorts 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 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 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 resembling lead time for
adjustments, 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 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 at least one month
In the future 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 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 utility or service you’re employed on, what
proportion of adjustments 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
In the future 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 would possibly present you that your launch course of
is quick and environment friendly. However solely attitudinal knowledge may let you know whether or not it
is easy and painless, which has necessary 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, and so they say “Properly, it seems such as you’re all good. There’s nothing
mistaken with you.” You’d be stunned! You’d say, “Wait, I’m telling
you that one thing feels mistaken.”
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 important advantages of qualitative metrics in the case of
measuring developer productiveness:
Qualitative metrics mean you can measure issues which are in any other case
unmeasurable
System metrics like lead time and deployment quantity seize what’s
taking place in our pipelines or ticketing methods. However there are numerous extra
elements of builders’ work that should be understood with a view 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 are in any other case troublesome or inconceivable 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,
hearken 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 ideally suited state. In different
phrases, human judgment is crucial.
Qualitative metrics present lacking visibility throughout groups and
methods
Metrics from ticketing methods and pipelines give us visibility into
a few of the work that builders do. However this knowledge alone can’t give us
the complete story. Builders do a number of work that’s not captured in tickets
or builds: for instance, designing key options, shaping the course of a
challenge, or serving to a teammate get onboarded.
It’s inconceivable to achieve visibility into all these actions by
knowledge from our methods alone. And even when we may theoretically acquire
all the information by methods, there are further challenges to capturing
metrics by instrumentation.
One instance is the problem of normalizing metrics throughout completely different
workforce workflows. For instance, when you’re making an attempt to measure how lengthy it takes
for duties to go from begin to completion, you would possibly attempt to get this knowledge
out of your ticketing instrument. However particular person groups typically have completely different
workflows that make it troublesome to supply an correct metric. In
distinction, merely asking builders how lengthy duties usually take could be
a lot easier.
One other widespread problem is cross-system visibility. For instance, a
small startup can measure TTR (time to revive) utilizing simply a problem
tracker resembling Jira. A big group, nevertheless, will doubtless have to
consolidate and cross-attribute knowledge throughout planning methods and deployment
pipelines with a view 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 danger, nevertheless, that the
full story isn’t being advised with out richer knowledge and that this will likely lead us
into specializing in the mistaken 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 critiques to be accomplished and incentivize groups to enhance
it. However this method could encourage unfavorable habits: reviewers dashing
by critiques or builders not discovering the suitable specialists to carry out
critiques.
Code critiques exist for an necessary goal: to make sure top quality
software program is delivered. If we do a extra holistic evaluation – specializing in the
outcomes of the method relatively than simply velocity – we discover that optimization
of code evaluate should guarantee good code high quality, mitigation of safety
dangers, constructing shared data throughout workforce members, in addition to making certain
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
necessary 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.
