Someplace, proper now, a know-how govt 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 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 accordance with these
benchmarks – however is there really 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 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’s, nonetheless, a greater strategy. An strategy that focuses on
capturing insights from builders themselves, reasonably 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
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 learn how to advocate for them. We comply with with sensible
steering on learn how to seize, observe, and make the most of this information.
Right now, developer productiveness is a essential concern for companies amid
the backdrop of fiscal tightening and transformational applied sciences reminiscent of
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 observe 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 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,” nonetheless, has no authoritative definition as famous within the
2019 journal paper What’s Qualitative in
Qualitative Analysis:
There are lots of definitions of qualitative analysis, but when we search for
a definition that addresses its distinctive function of being
“qualitative,” the literature throughout the broad area 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 are 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 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) 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 “mushy
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
might 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 selected 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 learn 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 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 group, 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 variety of our engineers additionally actually
admire 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 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 must 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 group can also be chargeable 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 information
for inspecting techniques. Nonetheless, we can not depend on this similar strategy 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 reason for opposition to qualitative information
are misconceptions which we deal with under. Later on this article, we
define the distinct advantages of self-reported information reminiscent of its means to
measure intangibles and floor essential context.
False impression: Qualitative information is barely subjective
Conventional office surveys usually give attention to the subjective
opinions and emotions of their workers. Thus many engineering leaders
intuitively consider that surveys can solely gather subjective information from
builders.
As we describe within the following part, surveys may 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
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. Because of this, many
office surveys don’t meet the minimal requirements wanted to provide
dependable or legitimate measures. Nicely designed surveys, nonetheless, produce
correct and dependable information (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 concern round how the info will probably be
used. In our expertise, when surveys are deployed as a instrument to assist
perceive and enhance bottlenecks affecting builders, there isn’t a
incentive for respondents to lie or recreation the system.
Whereas it’s true that survey information isn’t at all times 100% correct, we regularly
remind leaders that system metrics are sometimes imperfect too. For instance,
many organizations try and measure CI construct instances utilizing information aggregated
from their pipelines, solely to seek 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 sorts of qualitative metrics
There are two key sorts of qualitative metrics:
- 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 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 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 almost all tech practitioners overlook behavioral measures
when interested by 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 reminiscent of lead time for
adjustments, deployment frequency, and alter fail price. Unbeknownst to many,
DORA’s benchmarks are captured utilizing qualitative strategies with the survey
gadgets proven under:
Lead time
For the first software 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 operating in manufacturing)?
Greater than six months
One to 6 months
One week to at least one month
At some point 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 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 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
At some point 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 information
on the similar time is a strong 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 may let you know whether or not it
is easy 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
price, they usually say “Nicely, it seems to be such as you’re all good. There’s nothing
improper with you.” You’ll be bowled over! 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 information – it doesn’t deal with the primary goal
advantages that qualitative approaches can present.
There are three primary advantages of qualitative metrics in relation to
measuring developer productiveness:
Qualitative metrics let you measure issues which are 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 numerous extra
features of builders’ work that must be understood as a way 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 are in any other case troublesome or inconceivable to
measure.
An fascinating 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 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 not give us
the total story. Builders do a variety of work that’s not captured in tickets
or builds: for instance, designing key options, shaping the course of a
undertaking, or serving to a teammate get onboarded.
It’s inconceivable to achieve visibility into all these actions by way of
information from our techniques alone. And even when we may theoretically gather
all the info by way of techniques, there are further challenges to capturing
metrics by way of instrumentation.
One instance is the issue of normalizing metrics throughout completely different
group 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 information
out of your ticketing instrument. However particular person groups typically have completely different
workflows that make it troublesome to provide an correct metric. In
distinction, merely asking builders how lengthy duties usually take might 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 reminiscent of Jira. A big group, nonetheless, will probably must
consolidate and cross-attribute information throughout planning techniques and deployment
pipelines as a way to achieve end-to-end system visibility. This is usually a
yearlong effort, whereas capturing this information from builders can present a
baseline shortly.
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 threat, nonetheless, that the
full story isn’t being advised with out richer information and that this may occasionally lead us
into specializing in the improper factor.
One instance of that is code evaluation: a typical optimization is to attempt to
pace up the code evaluation. This appears logical as ready for a code evaluation
could cause wasted time or undesirable context switching. We may measure the
time it takes for opinions to be accomplished and incentivize groups to enhance
it. However this strategy could encourage unfavourable habits: reviewers speeding
by way of opinions or builders not discovering the suitable consultants to carry out
opinions.
Code opinions 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 reasonably than simply pace – we discover that optimization
of code evaluation 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 improvement
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
essential 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.
