Someplace, proper now, a know-how govt tells their administrators: “we
want a option 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 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 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 a number of the largest tech firms on this planet. It isn’t unusual
for measurement packages to fall brief 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, somewhat 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 find out how to advocate for them. We observe with sensible
steerage on find out how to seize, monitor, and make the most of this information.
At the moment, developer productiveness is a vital 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 choices 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 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 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 discipline of social science is
meager. The primary motive 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’t 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 ordinal scales which might 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 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 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 “gentle
metric”. We warning towards this phrase as a result of it harmfully and
incorrectly implies that information collected from people is weaker than “arduous
metrics” collected from programs. We additionally discourage the time period “subjective
metrics” as a result of it misconstrues the truth that information collected from people
could be both goal or subjective – as we talk about within the subsequent
part.
| 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 steerage on find out how to acquire and use
these measurements, however first we’ll present a real-world instance of this
method put to follow
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 plenty of our engineers additionally actually
respect this, that engineers aren’t robots, they’re people. And simply
fundamental numbers does 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 important 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, 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 programs. Nonetheless, we can not 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 inside “battle of the
metrics” which isn’t an excellent 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 information
are misconceptions which we deal with beneath. Later on this article, we
define the distinct advantages of self-reported information comparable to its capability to
measure intangibles and floor vital context.
False impression: Qualitative information is simply subjective
Conventional office surveys usually deal with the subjective
opinions and emotions of their workers. Thus many engineering leaders
intuitively consider that surveys can solely acquire subjective information from
builders.
As we describe within the following part, surveys can even seize
goal details about info 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. Consequently, many
office surveys don’t meet the minimal requirements wanted to supply
dependable or legitimate measures. Properly designed surveys, nevertheless, produce
correct and dependable information (we offer steerage on how to do that later in
the article).
Some organizations have issues that folks could lie in surveys. Which
can occur in conditions the place there’s concern round how the info will likely 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 information 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 instances utilizing information aggregated
from their pipelines, solely to seek out that it requires important effort to
clear the info (e.g. excluding background jobs, accounting for parallel
jobs) to supply an correct outcome
The 2 varieties of qualitative metrics
There are two key varieties 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 glad 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 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 the majority tech practitioners overlook behavioral measures
when serious 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
adjustments, deployment frequency, and alter fail price. Unbeknownst to many,
DORA’s benchmarks are captured utilizing qualitative strategies with the survey
gadgets 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 operating in manufacturing)?
Greater than six months
One to 6 months
One week to 1 month
In the future to 1 week
Lower than someday
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 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 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 1 month
In the future to 1 week
Lower than someday
Lower than one hour
We’ve discovered that the flexibility to gather attitudinal and behavioral information
on the identical time is a robust 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 might let you know 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 strain, your temperature, your coronary heart
price, they usually say “Properly, it appears to be like such as you’re all good. There’s nothing
fallacious with you.” You’ll be shocked! You’d say, “Wait, I’m telling
you that one thing feels fallacious.”
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 principle goal
advantages that qualitative approaches can present.
There are three foremost advantages of qualitative metrics with regards to
measuring developer productiveness:
Qualitative metrics can help you measure issues which might be in any other case
unmeasurable
System metrics like lead time and deployment quantity seize what’s
taking place in our pipelines or ticketing programs. However there are numerous extra
elements of builders’ work that must be understood so as to enhance
productiveness: for instance, whether or not builders are capable of keep within the move
or work or simply navigate their codebases. Qualitative metrics allow you to
measure these intangibles which might be in any other case troublesome or unimaginable 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 unimaginable resulting from the truth that
evaluation of technical debt depends on the comparability between the present
state of a system or codebase versus its imagined splendid state. In different
phrases, human judgment is important.
Qualitative metrics present lacking visibility throughout groups and
programs
Metrics from ticketing programs and pipelines give us visibility into
a number of the work that builders do. However this information alone can not give us
the total story. Builders do plenty of work that’s not captured in tickets
or builds: for instance, designing key options, shaping the route of a
mission, or serving to a teammate get onboarded.
It’s unimaginable to achieve visibility into all these actions by
information from our programs alone. And even when we might theoretically acquire
all the info by programs, there are further challenges to capturing
metrics by instrumentation.
One instance is the issue of normalizing metrics throughout completely different
group workflows. For instance, for those who’re attempting 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 supply an correct metric. In
distinction, merely asking builders how lengthy duties usually take could 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 probably must
consolidate and cross-attribute information throughout planning programs and deployment
pipelines so as 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 simple 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 information and that this will lead us
into specializing in the fallacious 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 might measure the
time it takes for critiques to be accomplished and incentivize groups to enhance
it. However this method could encourage damaging habits: reviewers dashing
by critiques or builders not discovering the best consultants to carry out
critiques.
Code critiques exist for an vital function: to make sure top quality
software program is delivered. If we do a extra holistic evaluation – specializing in the
outcomes of the method somewhat 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 guaranteeing
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 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 absolutely 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.
Learn how to seize qualitative metrics
Many tech practitioners don’t understand how troublesome it’s to put in writing good
survey questions and design good survey devices. In truth, there are
complete fields of research associated to this, comparable to psychometrics and
industrial psychology. You will need to convey or construct experience right here
when potential.
Under are few good guidelines for writing surveys to keep away from the most typical
errors we see organizations make:
- Survey gadgets must be rigorously worded and each query ought to solely ask
one factor. - If you wish to evaluate outcomes between surveys, watch out about altering
the wording of questions such that you simply’re measuring one thing completely different. - Should you change any wording, you have to do rigorous statistical assessments.
In survey parlance, ”good surveys” means “legitimate and dependable” or
“demonstrating good psychometric properties.” Validity is the diploma to
which a survey merchandise really measures the assemble you want to measure.
Reliability is the diploma to which a survey merchandise produces constant
outcomes out of your inhabitants and over time.
One mind-set about survey design that we’ve discovered useful to
tech practitioners: consider the survey response course of as an algorithm
that takes place within the human thoughts.
When a person is introduced a survey query, a sequence of psychological
steps happen so as to arrive at a response. The mannequin beneath is from
the seminal 2012 guide, The Psychology of Survey
Response:
| Part | Particular Processes |
|---|---|
| Comprehension |
Attend to questions and directions Characterize logical type of query Determine query focus (info sought) Hyperlink key phrases to related ideas |
| Retrieval |
Generate retrieval technique and cues Retrieve particular, generic recollections Fill in lacking particulars |
| Judgment |
Assess completeness and relevance of recollections Draw inferences based mostly on accessibility Combine materials retrieved Make estimate based mostly on partial retrieval |
| Response |
Map Judgement onto response class Edit response |
Decomposing the survey response course of and inspecting every step
might help us refine our inputs to supply extra correct survey outcomes.
Growing good survey gadgets requires rigorous design, testing, and
evaluation – similar to the method of designing software program!
However good survey design is only one side of operating profitable surveys.
Extra challenges embody participation charges, information evaluation, and figuring out
find out how to act on information. Under are a number of the finest practices we’ve
discovered.
Phase outcomes by group and persona
A standard mistake made by organizational leaders is to deal with companywide
outcomes as an alternative of knowledge damaged down by group and persona (e.g., position, tenure,
seniority). As beforehand described, developer expertise is very contextual
and might differ radically throughout groups or roles. Focusing solely on mixture
outcomes can result in overlooking issues that have an effect on small however vital
populations inside the firm, comparable to cellular builders.
Evaluate outcomes towards benchmarks
Comparative evaluation might help contextualize information and assist drive motion. For
instance, developer sentiment towards code high quality generally skews damaging, making
it troublesome to determine true issues or gauge their magnitude. The extra
actionable information level is: “are our builders extra annoyed about code
high quality than different groups or organizations?” Groups with decrease sentiment scores
than their friends and organizations with decrease scores than their business friends
can floor notable alternatives for enchancment.
Use transactional surveys the place applicable
Transactional surveys seize suggestions throughout particular touchpoints or
interactions within the developer workflow. For instance, platform groups can use
transactional surveys to immediate builders for suggestions whereas they’re within the midst of
creating a brand new service in an inside developer portal. Transactional surveys can
additionally increase information from periodic surveys by producing higher-frequency suggestions and
extra granular insights.
Keep away from survey fatigue
Many organizations wrestle to maintain excessive participation charges in surveys
over time. Lack of follow-up could cause builders to really feel that
repeatedly responding to surveys just isn’t worthwhile. It’s subsequently
vital that leaders and groups observe up and take significant motion after surveys.
Whereas a quarterly or
semi-annual survey cadence is perfect for many organizations, we’ve seen some
organizations achieve success with extra frequent surveys which might be built-in into
common group rituals comparable to retrospectives.
Survey Template
Under are a easy set of survey questions for getting began. Load the questions
beneath into your most popular survey instrument, or get began shortly by making a replica of our ready-to-go
Google Types template.
The template is deliberately easy, however surveys typically change into fairly sizable as your measurement
technique matures. For instance, Shopify’s developer survey is 20-minutes
lengthy and Google’s is over 30-minutes lengthy.
After you’ve got collected responses, rating the a number of selection questions
utilizing both imply or prime field scoring. Imply scores are calculated by
assigning every choice a price between 1 and 5 and taking the typical.
Prime field scores are calculated by the chances of responses that
select one of many prime two most favorable choices.
You’ll want to evaluate open textual content responses which might comprise nice
info. Should you’ve collected numerous feedback, LLM instruments
comparable to ChatGPT could be helpful for extracting core themes and
recommendations. Once you’ve completed analyzing outcomes, you’ll want to share
your findings with respondents so their time filling out the survey
feels worthwhile.
How simple or troublesome is it so that you can do work as a
developer or technical contributor at [INSERT ORGANIATION NAME]?
Very troublesome
Considerably troublesome
Neither simple nor troublesome
Considerably simple
Very simple
For the first utility or service you’re employed on, what
is your lead time for adjustments (that’s, how lengthy does it take to go
from code dedicated to code efficiently operating in
manufacturing)?
Multiple month
One week to 1 month
In the future to 1 week
Lower than someday
Lower than one hour
How typically do you are feeling extremely productive in your
work?
By no means
A bit of of the time
A number of the time
More often than not
All the time
Please price your settlement or disagreement with the next
statements:
| My group follows improvement finest practices | □ | □ | □ | □ | □ |
| I’ve sufficient time for deep work. | □ | □ | □ | □ | □ |
| I’m glad with the quantity of automated take a look at protection in my mission. |
□ | □ | □ | □ | □ |
| It is easy for me to deploy to manufacturing. | □ | □ | □ | □ | □ |
| I am glad with the standard of our CI/CD tooling. | □ | □ | □ | □ | □ |
| My group’s codebase is simple for me to contribute to. | □ | □ | □ | □ | □ |
| The quantity of technical debt on my group is acceptable based mostly on our objectives. | □ | □ | □ | □ | □ |
| Specs are constantly revisited and reprioritized in accordance with consumer indicators. | □ | □ | □ | □ | □ |
Please share any further suggestions on how your developer expertise might be improved
[open textarea]
Utilizing qualitative and quantitative metrics collectively
Qualitative metrics and quantitative metrics are complementary approaches
to measuring developer productiveness. Qualitative metrics, derived from
surveys, present a holistic view of productiveness that features each subjective
and goal measurements. Quantitative metrics, however, present
distinct benefits as nicely:
- Precision. People can let you know whether or not their CI/CD builds are typically
quick or gradual (i.e., whether or not durations are nearer to a minute or an hour), however
they can’t report on construct instances right down to millisecond precision. Quantitative
metrics are wanted when a excessive diploma of precision is required in our
measurements. - Continuity. Usually, the frequency at which a corporation can survey
their builders is at most a couple of times per quarter. As a way to acquire extra
frequent or steady metrics, organizations should collect information
systematically.
In the end, it’s by the mixture of qualitative and quantitative metrics – a mixed-methods method –
that organizations can achieve most visibility into the productiveness and
expertise of builders. So how do you employ qualitative and quantitative
metrics collectively?
We’ve seen organizations discover success after they begin with qualitative
metrics to determine baselines and decide the place to focus. Then, observe with
quantitative metrics to assist drill in deeper into particular areas.
Engineering leaders discover this method to be efficient as a result of qualitative
metrics present a holistic view and context, offering large understanding of
potential alternatives. Quantitative metrics, however, are
usually solely obtainable for a narrower set of the software program supply
course of.
Google equally advises its engineering leaders to go to survey information first
earlier than logs information because of this. Google engineering researcher
Ciera Jaspan explains: “We encourage leaders to go to the survey information first,
as a result of for those who solely take a look at logs information it does not actually let you know whether or not
one thing is nice or dangerous. For instance, we’ve a metric that tracks the time
to make a change, however that quantity is ineffective by itself. You do not know, is
this an excellent factor? Is it a nasty factor? Do we’ve an issue?”.
A blended strategies method permits us to reap the benefits of the advantages of
each qualitative and quantitative metrics whereas getting a full perceive of
developer productiveness:
- Begin with qualitative information to determine your prime alternatives
- As soon as you recognize what you need to enhance, use quantitative metrics to
drill-in additional - Observe your progress utilizing each qualitative and quantitative metrics
It’s only by combining as a lot information as potential – each qualitative and
quantitative – that organizations can start to construct a full understanding of
developer productiveness.
Ultimately, nevertheless, it’s vital to recollect: organizations spend loads
on extremely certified people that may observe and detect issues that log-based
metrics can’t. By tapping into the minds and voices of builders,
organizations can unlock insights beforehand seen as unimaginable.
