Someplace, proper now, a expertise govt tells their administrators: “we
want a solution 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 usually than
others. However I’m unsure if this spells a possibility for enchancment.”
If this story arc is acquainted to you, don’t fear – it is acquainted to
most, together with a few of the largest tech corporations on this planet. It’s not 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 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 find out how to advocate for them. We observe with sensible
steering on find out how to seize, observe, and make the most of this information.
Right this 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 considerations share is a reliance on measurement
to assist information selections and observe progress. And for this, qualitative
measurement is vital.
Be aware: 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 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 information
supplied by people. This can be 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 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,” 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 function of being
“qualitative,” the literature throughout the broad area 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” contains the time period metric, which means that the output is a
amount (i.e., a measurement). Second, high quality is often measured
by means of ordinal scales which might be translated into numerical values and
scores – which once more, contradicts the definition.
One other argument we’ve got heard is that the output of sentiment evaluation
is quantitative as a result of the evaluation leads to numbers. Whereas we agree
that the 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.
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 “laborious
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
might 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 particular 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 steering on find out how to accumulate and use
these measurements, however first we’ll present a real-world instance of this
strategy put to observe
Peloton is an American expertise firm
whose developer productiveness measurement technique facilities round
qualitative metrics. To gather qualitative metrics, their group
runs a semi-annual developer expertise survey led by their Tech
Enablement & Developer Expertise workforce, which is a part of their Product
Operations group.
Thansha Sadacharam, head of tech studying and insights, explains: “I
very strongly imagine, and I believe lots of our engineers additionally actually
admire this, that engineers aren’t robots, they’re people. And simply
taking a look at fundamental numbers does not drive the entire story. So for us, having
a extremely complete survey that helped us perceive that complete
developer expertise was actually necessary.”
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 information outcomes.
The Tech Enablement & Developer Expertise workforce can be accountable 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 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 great 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 information
are misconceptions which we tackle beneath. Later on this article, we
define the distinct advantages of self-reported information comparable to its skill to
measure intangibles and floor vital context.
False impression: Qualitative information is simply subjective
Conventional office surveys usually give attention to the subjective
opinions and emotions of their staff. Thus many engineering leaders
intuitively imagine that surveys can solely accumulate subjective information 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 superb concrete
instance.
Some examples of goal survey questions:
- How lengthy does it take to go from code dedicated to code efficiently
working in manufacturing? - How usually does your group deploy code to manufacturing or
launch it to finish customers?
False impression: Qualitative 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. Effectively designed surveys, nevertheless, 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 worry round how the information will likely be
used. In our expertise, when surveys are deployed as a software to assist
perceive and enhance bottlenecks affecting builders, there isn’t any
incentive for respondents to lie or sport the system.
Whereas it’s true that survey information isn’t all the time 100% correct, we frequently
remind leaders that system metrics are sometimes imperfect too. For instance,
many organizations try to measure CI construct occasions utilizing information aggregated
from their pipelines, solely to seek out that it requires vital effort to
clear the information (e.g. excluding background jobs, accounting for parallel
jobs) to supply an correct end result
The 2 varieties of qualitative metrics
There are two key varieties 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 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 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 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 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 working 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 per thirty days and as soon as each six months
Between as soon as per week and as soon as per thirty 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
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 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 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
price, and so they say “Effectively, it seems 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 tackle the primary goal
advantages that qualitative approaches can present.
There are three essential advantages of qualitative metrics in the case of
measuring developer productiveness:
Qualitative metrics assist you to measure issues which might be 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 numerous extra
elements of builders’ work that have to be understood in an effort to enhance
productiveness: for instance, whether or not builders are in a position to keep within the circulation
or work or simply navigate their codebases. Qualitative metrics allow you to
measure these intangibles which might be in any other case tough or unattainable 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 unattainable 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 superb state. In different
phrases, human judgment is crucial.
Qualitative metrics present lacking visibility throughout groups and
programs
Metrics from ticketing programs and pipelines give us visibility into
a few of the work that builders do. However this information alone can not give us
the total story. Builders do lots of work that’s not captured in tickets
or builds: for instance, designing key options, shaping the route of a
venture, or serving to a teammate get onboarded.
It’s unattainable to realize visibility into all these actions by means of
information from our programs alone. And even when we might theoretically accumulate
all the information by means of programs, there are extra challenges to capturing
metrics by means of instrumentation.
One instance is the problem of normalizing metrics throughout totally 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 information
out of your ticketing software. However particular person groups usually have totally different
workflows that make it tough to supply an correct metric. In
distinction, merely asking builders how lengthy duties usually take might 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 difficulty
tracker comparable to Jira. A big group, nevertheless, will probably must
consolidate and cross-attribute information throughout planning programs and deployment
pipelines in an effort to achieve end-to-end system visibility. This could be 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 simple to focus closely on quantitative measures.
They appear clear and clear, afterall. There’s a threat, nevertheless, that the
full story isn’t being advised with out richer information and that this may increasingly 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
may cause wasted time or undesirable context switching. We might measure the
time it takes for evaluations to be accomplished and incentivize groups to enhance
it. However this strategy could encourage unfavourable habits: reviewers dashing
by means of evaluations or builders not discovering the suitable specialists to carry out
evaluations.
Code evaluations exist for an necessary goal: to make sure prime 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 workforce 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 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 absolutely using the concepts the
builders are bringing, whether or not they really feel secure 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 tough it’s to jot down good
survey questions and design good survey devices. In actual fact, there are
entire fields of research associated to this, comparable to psychometrics and
industrial psychology. It is very important convey or construct experience right here
when attainable.
Beneath are few good guidelines for writing surveys to keep away from the most typical
errors we see organizations make:
- Survey gadgets have to be fastidiously 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 totally different. - Should you change any wording, you could 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 really 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 introduced a survey query, a collection of psychological
steps happen in an effort to arrive at a response. The mannequin beneath is from
the seminal 2012 ebook, 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 reminiscences Fill in lacking particulars |
| Judgment |
Assess completeness and relevance of reminiscences Draw inferences primarily based on accessibility Combine materials retrieved Make estimate primarily based 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 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 working profitable surveys.
Extra challenges embody participation charges, information evaluation, and realizing
find out how to act on information. Beneath are a few of the finest practices we’ve
discovered.
Section outcomes by workforce and persona
A typical mistake made by organizational leaders is to give attention to companywide
outcomes as an alternative of information damaged down by workforce and persona (e.g., function, tenure,
seniority). As beforehand described, developer expertise is very contextual
and might differ radically throughout groups or roles. Focusing solely on combination
outcomes can result in overlooking issues that have an effect on small however necessary
populations throughout the firm, comparable 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 unfavourable, making
it tough 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 acceptable
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 battle to maintain excessive participation charges in surveys
over time. Lack of follow-up may 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 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 workforce rituals comparable to retrospectives.
Survey Template
Beneath are a easy set of survey questions for getting began. Load the questions
beneath into your most well-liked survey software, or get began rapidly by making a duplicate of our ready-to-go
Google Kinds template.
The template is deliberately easy, however surveys usually turn 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 have collected responses, rating the a number of alternative questions
utilizing both imply or prime field scoring. Imply scores are calculated by
assigning every possibility a price between 1 and 5 and taking the typical.
High field scores are calculated by the odds of responses that
select one of many prime two most favorable choices.
Be sure you evaluation open textual content responses which may comprise nice
info. Should you’ve collected a lot of feedback, LLM instruments
comparable to ChatGPT might be helpful for extracting core themes and
solutions. Once you’ve completed analyzing outcomes, make sure you share
your findings with respondents so their time filling out the survey
feels worthwhile.
How simple or tough is it so that you can do work as a
developer or technical contributor at [INSERT ORGANIATION NAME]?
Very tough
Considerably tough
Neither simple nor tough
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 working 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 are feeling extremely productive in your
work?
By no means
Just a little of the time
Among the time
More often than not
All the time
Please price your settlement or disagreement with the next
statements:
| My workforce 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 venture. |
□ | □ | □ | □ | □ |
| It is simple for me to deploy to manufacturing. | □ | □ | □ | □ | □ |
| I am glad with the standard of our CI/CD tooling. | □ | □ | □ | □ | □ |
| My workforce’s codebase is simple for me to contribute to. | □ | □ | □ | □ | □ |
| The quantity of technical debt on my workforce is suitable primarily based on our targets. | □ | □ | □ | □ | □ |
| Specs are constantly revisited and reprioritized in accordance with person indicators. | □ | □ | □ | □ | □ |
Please share any extra 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 effectively:
- Precision. People can let you know whether or not their CI/CD builds are usually
quick or gradual (i.e., whether or not durations are nearer to a minute or an hour), however
they can’t report on construct occasions right 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 company can survey
their builders is at most a couple of times per quarter. In an effort to accumulate extra
frequent or steady metrics, organizations should collect information
systematically.
In the end, it’s by means of the mix of qualitative and quantitative metrics – a mixed-methods strategy –
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 ascertain baselines and decide the place to focus. Then, observe 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 huge understanding of
potential alternatives. Quantitative metrics, however, are
usually solely out there 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 taking a look at 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 when you solely take a look at logs information it does not actually let you know whether or not
one thing is sweet or unhealthy. For instance, we’ve got a metric that tracks the time
to make a change, however that quantity is ineffective by itself. You do not know, is
this a great factor? Is it a nasty factor? Do we’ve got an issue?”.
A combined strategies strategy permits us to benefit from 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 - Monitor your progress utilizing each qualitative and quantitative metrics
It’s only by combining as a lot information as attainable – each qualitative and
quantitative – that organizations can start to construct a full understanding of
developer productiveness.
In the long run, nevertheless, it’s necessary to recollect: organizations spend so much
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 unattainable.
