Someplace, proper now, a expertise 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 keeping with 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 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 among the largest tech corporations on this planet. It’s not unusual
for measurement packages to fall brief 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, fairly than solely counting on
fundamental measures of velocity 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 how one can advocate for them. We comply with with sensible
steering on how one can seize, observe, and make the most of this information.
In the present day, developer productiveness is a vital concern for companies amid
the backdrop of fiscal tightening and transformational applied sciences corresponding 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 considerations share is a reliance on measurement
to assist information selections and observe 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 advocate that organizations use the time period “developer
expertise,” which has extra constructive connotations for builders.
What’s a qualitative metric?
We outline a qualitative metric as a measurement comprised of information
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 is Qualitative in
Qualitative Research:
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 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 means that the output is a
amount (i.e., a measurement). Second, high quality is often measured
by ordinal scales which are 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 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 “tender
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 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 focus on within the subsequent
part.
| Kind | Definition | Instance |
|---|---|---|
| Attitudinal metrics | Subjective emotions, opinions, or attitudes towards a selected topic. | How happy are you together 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 how one can 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 crew, 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 does not drive the entire story. So for us, having
a very complete survey that helped us perceive that total
developer expertise was actually vital.”
Every survey is distributed 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 information outcomes.
The Tech Enablement & Developer Expertise crew can also 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 in regards to 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’t depend on this similar strategy 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 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 behind opposition to qualitative information
are misconceptions which we tackle beneath. Later on this article, we
define the distinct advantages of self-reported information corresponding to its capacity to
measure intangibles and floor vital context.
False impression: Qualitative information is simply 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 gather subjective information from
builders.
As we describe within the following part, surveys can even seize
goal details about information 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 usually 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 individuals 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, nonetheless, produce
correct and dependable information (we offer steering on how to do that later in
the article).
Some organizations have considerations that individuals might lie in surveys. Which
can occur in conditions the place there’s worry 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 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 occasions 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 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 selected topic. An instance of an attitudinal measure would
be the numeric worth captured in response to the query: “How happy are
you together 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 enthusiastic 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 corresponding to 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 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 in the future
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 monthly and as soon as each six months
Between as soon as per week and as soon as monthly
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 share
For the first utility or service you’re employed on, what
share 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 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
At some point to at least one week
Lower than in the future
Lower than one hour
We’ve discovered that the power to gather attitudinal and behavioral information
on the similar time is a strong advantage of qualitative measurement.
For instance, behavioral information may present you that your launch course of
is quick and environment friendly. However solely attitudinal information might inform you whether or not it
is easy 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 strain, your temperature, your coronary heart
charge, and so they say “Properly, it appears to be like such as you’re all good. There’s nothing
mistaken with you.” You’ll be bowled over! 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 information – it doesn’t tackle the principle goal
advantages that qualitative approaches can present.
There are three principal advantages of qualitative metrics in relation to
measuring developer productiveness:
Qualitative metrics assist you to 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 programs. However there are various extra
facets of builders’ work that have to 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 are in any other case troublesome or not possible to
measure.
An fascinating instance of that is technical debt. At Google, a examine to
establish 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 not possible on account of the truth that
evaluation of technical debt depends on the comparability between the present
state of a system or codebase versus its imagined perfect 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
among the work that builders do. However this information alone can’t give us
the complete story. Builders do a variety of work that’s not captured in tickets
or builds: for instance, designing key options, shaping the route of a
challenge, or serving to a teammate get onboarded.
It’s not possible to achieve visibility into all these actions by
information from our programs alone. And even when we might theoretically gather
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
crew 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 information
out of your ticketing instrument. However particular person groups usually have completely different
workflows that make it troublesome to provide an correct metric. In
distinction, merely asking builders how lengthy duties usually take could 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 corresponding to Jira. A big group, nonetheless, will probably must
consolidate and cross-attribute information throughout planning programs and deployment
pipelines so as to acquire end-to-end system visibility. This generally is a
yearlong effort, whereas capturing this information from builders can present a
baseline rapidly.
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 danger, nonetheless, that the
full story isn’t being instructed with out richer information and that this may increasingly 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
could cause wasted time or undesirable context switching. We might measure the
time it takes for opinions to be accomplished and incentivize groups to enhance
it. However this strategy might encourage destructive conduct: reviewers dashing
by opinions or builders not discovering the suitable specialists to carry out
opinions.
Code opinions exist for an vital goal: to make sure top quality
software program is delivered. If we do a extra holistic evaluation – specializing in the
outcomes of the method fairly than simply velocity – we discover that optimization
of code evaluate should guarantee good code high quality, mitigation of safety
dangers, constructing shared data throughout crew members, in addition to guaranteeing
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 crew 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.
Easy methods to seize qualitative metrics
Many tech practitioners don’t notice how troublesome it’s to write down good
survey questions and design good survey devices. In reality, there are
entire fields of examine associated to this, corresponding to psychometrics and
industrial psychology. You will need to carry or construct experience right here
when potential.
Under are few good guidelines for writing surveys to keep away from the commonest
errors we see organizations make:
- Survey objects have to be rigorously worded and each query ought to solely ask
one factor. - If you wish to examine outcomes between surveys, watch out about altering
the wording of questions such that you just’re measuring one thing completely different. - If you happen to change any wording, you will need to do rigorous statistical exams.
In survey parlance, ”good surveys” means “legitimate and dependable” or
“demonstrating good psychometric properties.” Validity is the diploma to
which a survey merchandise truly measures the assemble you need 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 offered a survey query, a collection of psychological
steps happen so as to arrive at a response. The mannequin beneath is from
the seminal 2012 e-book, The Psychology of Survey
Response:
| Element | Particular Processes |
|---|---|
| Comprehension |
Attend to questions and directions Symbolize logical type of query Establish query focus (info sought) Hyperlink key phrases to related ideas |
| Retrieval |
Generate retrieval technique and cues Retrieve particular, generic reminiscences Fill in lacking particulars |
| Judgment |
Assess completeness and relevance of reminiscences 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
will help us refine our inputs to provide extra correct survey outcomes.
Creating good survey objects requires rigorous design, testing, and
evaluation – identical to the method of designing software program!
However good survey design is only one side of operating profitable surveys.
Extra challenges embrace participation charges, information evaluation, and realizing
how one can act on information. Under are among the greatest practices we’ve
discovered.
Phase outcomes by crew and persona
A typical mistake made by organizational leaders is to concentrate on companywide
outcomes as a substitute of information damaged down by crew and persona (e.g., function, tenure,
seniority). As beforehand described, developer expertise is very contextual
and may 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, corresponding to cell builders.
Evaluate outcomes towards benchmarks
Comparative evaluation will help contextualize information and assist drive motion. For
instance, developer sentiment towards code high quality generally skews destructive, making
it troublesome to establish true issues or gauge their magnitude. The extra
actionable information level is: “are our builders extra pissed off 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 inner 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 will not be worthwhile. It’s due to this fact
vital that leaders and groups comply with 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 are built-in into
common crew rituals corresponding 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 rapidly by making a replica of our ready-to-go
Google Forms template.
The template is deliberately easy, however surveys usually develop 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 possibility a worth between 1 and 5 and taking the common.
High field scores are calculated by the odds of responses that
select one of many prime two most favorable choices.
You’ll want to evaluate open textual content responses which might include nice
info. If you happen to’ve collected a lot of feedback, LLM instruments
corresponding to ChatGPT could be helpful for extracting core themes and
options. If you’ve completed analyzing outcomes, make sure you share
your findings with respondents so their time filling out the survey
feels worthwhile.
How straightforward 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 straightforward nor troublesome
Considerably straightforward
Very straightforward
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)?
A couple of month
One week to at least one month
At some point to at least one week
Lower than in the future
Lower than one hour
How usually do you’re feeling extremely productive in your
work?
By no means
A little bit of the time
Among the time
More often than not
All the time
Please charge your settlement or disagreement with the next
statements:
| My crew follows growth greatest practices | □ | □ | □ | □ | □ |
| I’ve sufficient time for deep work. | □ | □ | □ | □ | □ |
| I’m happy with the quantity of automated check protection in my challenge. |
□ | □ | □ | □ | □ |
| It is simple for me to deploy to manufacturing. | □ | □ | □ | □ | □ |
| I am happy with the standard of our CI/CD tooling. | □ | □ | □ | □ | □ |
| My crew’s codebase is straightforward for me to contribute to. | □ | □ | □ | □ | □ |
| The quantity of technical debt on my crew is suitable based mostly on our targets. | □ | □ | □ | □ | □ |
| Specs are constantly revisited and reprioritized in keeping with consumer indicators. | □ | □ | □ | □ | □ |
Please share any further suggestions on how your developer expertise could possibly 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, alternatively, present
distinct benefits as nicely:
- Precision. People can inform you whether or not their CI/CD builds are usually
quick or sluggish (i.e., whether or not durations are nearer to a minute or an hour), however
they can not report on construct occasions all the way down to millisecond precision. Quantitative
metrics are wanted when a excessive diploma of precision is required in our
measurements. - Continuity. Sometimes, the frequency at which a corporation can survey
their builders is at most a few times per quarter. With the intention to gather extra
frequent or steady metrics, organizations should collect information
systematically.
Finally, it’s by the mix of qualitative and quantitative metrics – a mixed-methods strategy –
that organizations can acquire 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 once they begin with qualitative
metrics to ascertain baselines and decide the place to focus. Then, comply with with
quantitative metrics to assist drill in deeper into particular areas.
Engineering leaders discover this strategy to be efficient as a result of qualitative
metrics present a holistic view and context, offering vast understanding of
potential alternatives. Quantitative metrics, alternatively, 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 should you solely take a look at logs information it does not actually inform you whether or not
one thing is sweet or unhealthy. 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 a very good factor? Is it a nasty factor? Do we’ve an issue?”.
A blended strategies strategy permits us to make the most of the advantages of
each qualitative and quantitative metrics whereas getting a full perceive of
developer productiveness:
- Begin with qualitative information to establish your prime alternatives
- As soon as you already know what you wish to enhance, use quantitative metrics to
drill-in additional - Monitor 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.
In the long run, nonetheless, it’s vital to recollect: organizations spend rather a lot
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 not possible.
