Utilizing the Strangler Fig with Cell Apps
On this article we intention to indicate why taking an incremental strategy to
legacy cellular software modernization will be preferable to the classical
‘rewrite from scratch’ methodology. Thoughtworks has the good thing about working with
massive enterprise shoppers which can be depending on their in-house cellular
purposes for his or her core enterprise. We see a lot of them asking their
purposes to do extra and evolve quicker, whereas on the similar time, we see an
rising rejection of reputationally damaging excessive threat releases.
As an answer, this text proposes various strategies of legacy
modernization which can be primarily based in Area Pushed Design and hinge on the
software of the Strangler Fig sample. Whereas these ideas are removed from
new, we consider that their utilization in cellular purposes are novel. We really feel
that regardless of incurring a bigger short-term overhead from their utilization, that is
a suitable tradeoff. We assert how the methodology is used to fight the
aforementioned attitudinal shifts in legacy cellular software growth
whereas gaining a platform to decrease threat and drive incremental worth
supply.
We talk about how this works in idea, diving into each the structure
and code. We additionally recount how this labored in observe when it was trialled on
a big, legacy cellular software at one in every of Thoughtworks’ enterprise
shoppers. We spotlight how the sample enabled our consumer to quickly construct,
take a look at and productionize a modernized subset of area functionalities inside
an present legacy software.
We transfer on to guage the effectiveness of the trial by highlighting the enterprise
dealing with advantages akin to a signficantly quicker time to worth and a 50% decreased median cycle
time. We additionally contact on different anticipated advantages that ought to be used to
measure the success of this system.
The Drawback with Cell Legacy Modernization
As purposes age and develop, they have a tendency to deteriorate each in high quality
and efficiency. Options take longer to get to market whereas outages
and rolled again releases turn into extra extreme and frequent. There’s a
nuanced complexity to be understood concerning the explanation why this
happens each on the code and organizational stage.
To summarize although, sooner or later, an
group will develop uninterested in the poor outcomes from their
software program and begin the method of legacy substitute. The choice
to exchange could also be made primarily based on a number of elements, together with (however not restricted to)
price/profit evaluation, threat evaluation, or alternative price. Finally a legacy modernization technique might be chosen.
This might be depending on the group’s perspective to threat. For
instance, a posh, excessive availability system might demand a extra
incremental or interstitial strategy to legacy
substitute/displacement than an easier, much less enterprise vital one.
Within the case of cellular software modernization, these choices have
in current reminiscence been moderately clear lower. A cellular software was
usually designed to do a person thing- Apple’s “There’s an app for
that” nonetheless rings out loud and clear in individuals’s minds 15 years after
the preliminary batch of ads. That message was one which was taken
to coronary heart by organizations and startups alike: If you want to do
one thing, write an app to do it. If you want to do one thing else, write
one other app to try this. This instance struck me once I was
pruning the apps on my telephone a few years in the past. On the time I observed I
had a number of apps from the producer of my automobile; an older one and a more recent
one. I additionally had two apps from my financial institution; one confirmed my checking account,
one other that analyzed and illustrated my spending habits. I had three apps
from Samsung for varied IoT gadgets, and at the least two from Philips that
managed my toothbrush and lightweight bulbs. The purpose I’m laboring right here is
{that a} cellular software was by no means allowed to get so difficult,
that it couldn’t be torn down, cut up out or began from scratch once more.
However what occurs when this isn’t the case? Absolutely not all apps are
created equal? Many consider that the cellular expertise of the long run
might be centered round so-called
“super-apps”; apps the place you may pay, socialize, store, name,
message, and recreation, all below one software. To a point this has
already occurred in China with “do-everything” purposes like
‘WeChat’ and ‘AliPay’- we see the cellular system and its working
system as extra of a automobile to permit the operating of those gigantic
items of software program. Feedback from trade point out a realization
that the West
is not quite as far along as China in this regard. However whereas not
on the super-app, there isn’t any doubt that complexity of the cellular
app expertise as a complete has elevated considerably in current
years. Take the instance of YouTube, when first put in, again in
the early 2010’s, the applying might play movies and never a lot
else. Opening the applying right this moment one is introduced with “Movies”
and “Shorts”, a information feed, controllable classes, subscriptions,
to not point out a content material enhancing and publishing studio. Equally
with the Uber app, the consumer is requested in the event that they wish to order meals.
Google Maps can present a 3D view of a road and Amazon now recommends
scrollable product-recommendation temper boards. These further options
have actually enriched a consumer’s expertise however in addition they make the
conventional construct, use, rebuild method way more tough.
This problem will be defined by contemplating a few of the present
widespread issues of cellular software growth:
- Huge View Controllers/Actions/Fragments
- Direct manipulation of UI components
- Platform particular code
- Poor Separation of Issues
- Restricted Testability
With self-discipline, these issues will be managed early on. Nonetheless, with
a big software that has grown chaotically inline with the enterprise it
helps, incremental change might be tough regardless. The answer then, as
earlier than, is to construct new and launch . However what if you happen to solely need
so as to add a brand new function, or modernize an present area? What if you wish to
take a look at your new function with a small group of customers forward of time whereas
serving everybody else the previous expertise? What if you happen to’re joyful together with your
app retailer critiques and don’t wish to threat impacting them?
Taking an incremental strategy to app substitute then is the important thing to
avoiding the pitfalls related to ‘massive bang releases’. The Strangler
Fig sample is usually used to rebuild a legacy software in
place: a brand new system is regularly created across the edges of an previous
one by way of frequent releases. This sample is well-known, however
not broadly utilized in a cellular context. We consider the explanation for that is that there are a number of conditions that must be in
place earlier than diving headfirst into the sample.
Of their article on Patterns
of Legacy Displacement, the authors describe 4 broad
classes (conditions) used to assist break a legacy drawback into
smaller, deliverable components:
- Perceive the outcomes you wish to obtain
- Determine how you can break the issue up into smaller components
- Efficiently ship the components
- Change the group to permit this to occur on an ongoing
foundation
Solely within the third level, can we envisage the invocation of the Strangler Fig
sample. Doing so with out an understanding of why, what or the way it may
proceed sooner or later is a recipe for failure.
Going ahead, the article charts how Thoughtworks was capable of assist one
of its enterprise shoppers develop its present cellular legacy modernization
efforts right into a profitable experiment that demonstrated the worth behind
using the Strangler Fig sample in a cellular context.
Satisfying the Conditions
At this level, it appears applicable to introduce the consumer that
impressed the writing of this text – a globally distributed enterprise
with a longtime retail group that had embraced cellular
purposes for a few years. Our consumer had realized the advantages an
app introduced to offer a self-service expertise for his or her
merchandise. They’d shortly expanded and developed their app domains to permit tens of millions
of consumers to take full benefit of all of the merchandise they offered.
The group had already spent a major period of time and
effort modernizing its cellular purposes in its smaller
sub-brands. Responding to an absence of reuse/vital duplication of
efforts, high
cognitive load in app groups and gradual function supply, the
group selected a cellular know-how stack that leveraged a
Modular Micro-app structure. This technique had been largely
profitable for them, enabling proliferation of options widespread to
the group (e.g. ‘login/registration/auth’ or ‘grocery purchasing’)
throughout totally different manufacturers and territories, in a fraction of the time it
would have taken to write down all of them individually.
The diagram above is a simplified illustration of the modular
structure the group had efficiently carried out. React
Native was used attributable to its potential to thoroughly encapsulate a
area’s bounded context inside an importable part. Every
part was underpinned by its personal backend
for frontend (BFF) that got here with the infrastructure as code to
instantiate and run it. The host apps, proven above as UK and US,
had been merely containers that supplied the app particular configuration
and theming to the person micro-apps. This ‘full slice’ of
performance has the benefits of each permitting re-use and
lowering complexity by abstracting software domains to micro-apps
managed by particular person groups. We converse in depth concerning the outcomes of
this structure within the already referenced article on ‘Linking
Modular Structure’.
As touched upon earlier, the group’s cellular property was made up of
a variety of smaller sub-brands that served related merchandise in different
territories. With the modular structure sample tried and examined, the
group wished to focus efforts on its ‘home-territory’ cellular
software (serving its most important model). Their most important cellular app was a lot
bigger when it comes to function richness, income and consumer volumes to that of
the sub manufacturers. The app had been gaining options and customers over many
years of product growth. This regular however vital development had
introduced success when it comes to how well-regarded their software program was on each
Google and Apple shops. Nonetheless, it additionally began to indicate the
attribute indicators of decay. Change frequency within the software
had moved from days to months, leading to a big product backlog and
pissed off stakeholders who wished an software that would evolve as
quick as their merchandise did. Their lengthy launch cycle was associated to threat
aversion: Any outage within the software was a severe lack of income to
the group and in addition brought about their clients misery because of the
important nature of the merchandise they offered. Modifications had been all the time examined
exhaustively earlier than being put stay.
The group first thought-about a rewrite of the complete software
and had been shocked by the fee and period of such a challenge. The potential
unfavourable reception of a ‘massive bang’ new launch to their app retailer
clients additionally brought about issues within the ranges of threat they may settle for.
Recommendations of alpha and beta consumer teams had been thought-about unacceptable
given the massive volumes of customers the group was serving. On this
occasion, a modernization effort much like that seen of their sub-brands
was believed to be of significantly increased price and threat.
Thoughtworks advised an preliminary proof of idea that constructed on the
successes of the reusability already seen with a modular
structure. We addressed the group’s massive bang threat aversion
by suggesting the Strangler
Fig sample to incrementally exchange particular person domains. By
leveraging each strategies collectively we had been capable of give the
group the power to reuse production-ready domains from
their modernized cellular apps inside their legacy app expertise. The
thought was to ship worth into the arms of consumers a lot sooner
with much less duplication than in a full rewrite. Our focus was not on
delivering probably the most lovely or cohesive full app expertise (-not
fairly but anyway). It was about acquiring confidence each within the
stability of the iterative substitute sample and in addition in how effectively
the brand new product was being acquired. These items of knowledge
allowed the group to make extra knowledgeable product choices
early on within the modernization course of. This ensured the completed product
had been extensively used and molded by the precise finish customers.
Strangler Fig and Micro-apps
So how far did we get with the proof of idea and extra importantly
how did we really do that? Taking the learnings from Modular Micro-app
structure (described above), we theorized the design to be as follows:
The preliminary state of the applying concerned the identification of
domains and their navigation routes (Determine how you can break the issue into
smaller components). We targeted our efforts on discovering navigation entry factors
to domains, we referred to as them our ‘factors of interception’. These acquainted
with cellular software growth will know that navigation is usually
a effectively encapsulated concern, which means that we could possibly be assured that we
might all the time direct our customers to the expertise of our selecting.
As soon as we recognized our ‘factors of interception’, we chosen a website
for incremental substitute/retirement. Within the instance above we concentrate on
the Grocery area throughout the present software. The ‘new‘ Grocery area,
was a micro-app that was already getting used throughout the sub-brand apps. The
key to implementation of the Strangler Fig sample concerned embedding an
whole React Native software inside the prevailing legacy software.
The staff took the chance to observe the nice modularity practices that
the framework encourages and constructed Grocery as an encapsulated part. This
meant that as we added extra domains to our Strangler Fig Embedded
Utility, we might management their enablement on a person stage.
As per the diagram, within the legacy app, Grocery performance was
underpinned by a monolithic backend. Once we imported the New Grocery
Micro-app, it was configured to make use of that very same monolithic backend. As
talked about beforehand, every micro-app got here with its personal Backend for
Frontend (BFF). On this occasion, the BFF was used as an anti-corruption
layer; creating an isolating layer to take care of the identical area mannequin as
the frontend. The BFF talked to the prevailing monolith by way of the identical
interfaces the legacy cellular software did. Translation between each
monolith and micro-app occurred in each instructions as mandatory. This
allowed the brand new module’s frontend to not be constrained by the legacy API
because it developed.
We continued the within out substitute of the previous software by
repeating the method once more on the following prioritized area. Though out
of scope for this proof of idea, the intention was that the method
proven be repeated till the native software is finally only a shell
containing the brand new React Native software. This then would permit the elimination of the
previous native software totally, leaving the brand new one as a replacement. The brand new
software is already examined with the prevailing buyer base, the
enterprise has confidence in its resilience below load, builders discover it
simpler to develop options and most significantly, unacceptable dangers
related to a typical massive bang launch had been negated.
Diving Deeper…
To this point we’ve introduced a really broad set of diagrams to
illustrate our Cell Strangler Fig idea. Nonetheless, there are
nonetheless many
excellent implementation-focused questions with the intention to take idea
into
observe.
Implanting the Strangler Fig
A very good begin may be, how did we summary the complexity of
constructing each native and non-native codebases?
Beginning with the repository construction, we turned our unique native
software construction inside out. By inverting the management
of the native software to a React Native (RN) software
we prevented vital duplication related to nesting
our RN listing twice inside every cellular working system’s
folder. Actually, the react-native init default
template gave a construction to embed our iOS and Android
subfolders.
From a developer perspective, the code was largely unchanged. The
legacy software’s two operating-system-separated groups had been capable of
goal their unique directories, solely this time it was inside a single
repository. The diagram under is a generalized illustration (that’s,
relevant to each iOS and Android) of the present pipeline from the
Consumer as we understood:
Bi-Directional Communication utilizing the Native Bridge
We’ve already touched on navigation with our beforehand talked about
‘factors of interception’. It’s price wanting deeper into how we
facilitated communication and the switch of management between native and
React Native as it will be simple to oversimplify this space.
The React
Native ‘Bridge’ allows communication between each
worlds. Its goal is to function the message queue for
directions like rendering views, calling native features,
occasion handlers, passing values and so forth. Examples of
properties handed throughout the bridge can be isCartOpen
or sessionDuration. Whereas an instance of a bridge
operate name may be js invocations of the system’s native geolocation
module.
The diagram above additionally references the idea of a ‘React Native
Micro App’. We launched this idea earlier within the article after we
described our app when it comes to journeys. To recap although, a micro-app is a self-contained
encapsulation of UI and performance associated to a single
area. A React Native app could also be made up of many micro-apps
much like the micro
frontend sample. Along with these benefits we’ve already mentioned, it additionally permits us to have a higher
diploma of management over how our Strangler Fig software
grows and is interacted with. For instance, in a scenario
the place we’ve extra confidence in one in every of our new journeys
than one other we’re afforded the choice to divert a bigger
proportion of visitors to at least one micro-app with out impacting
one other.
Bringing each ideas collectively, we utilized the bridge to
seamlessly transfer our customers backwards and forwards throughout experiences.
The flexibility to cross data allowed us to protect any
rapid state or motion from the UI that wanted to
persevere throughout experiences. This was notably helpful
in our case because it helped us to decouple domains at
applicable fracture factors with out worrying whether or not we
would lose any native state after we crossed the bridge.
Dealing with Delicate Knowledge
To this point we’ve mentioned transferring between legacy and new codebases as
atomic entities. We’ve touched on how native state will be
shared throughout the bridge, however what about extra delicate
information? Having just lately changed their login and registration (auth)
course of of their different customer-facing React Native apps
with a modular, configurable, model agnostic one, the consumer
was eager for us to reuse that have. We set ourselves
the duty of integrating this expertise as an
preliminary demonstration of the Strangler Fig sample in
motion.
We leveraged the strategies already discussed to implant the
Strangler Fig: i.e. the brand new authentication journey on the
React Native aspect. When a buyer efficiently logged in or
registered, we wanted to make sure that in the event that they moved away from
the brand new expertise (again into the legacy journey), their
authentication standing was preserved irrespective of the place they
had been.
For this, we utilized the native module code calling aspect of the
bridge. The diagram above explains how we achieved this by
utilizing a React Native library that served as a wrapper to
save authentication information to the Android
EncryptedSharedPreferences or iOS Keychain after a
profitable login. Because of the versatile construction of the information
contained in the keystore, it allowed us to seamlessly share the
(re)authentication course of regardless of whether or not
the consumer was within the native or non-native expertise. It additionally
gave us a sample for the safe sharing of any delicate
information between experiences.
Regression Testing at Area Boundaries
An essential a part of a cutover technique is the power to know
from any vantage level (in our case, totally different groups working throughout the similar app) whether or not a change made affected the
general performance of the system. The embedded app
sample described above presents a novel problem on this
regard round scalable testability of a multi-journey
expertise. Furthermore one that’s managed by a number of groups
with quite a few branching paths.
The interplay diagram above reveals an instance journey circulation
throughout the embedded app. One factor to note is the quantity
of branching complexity throughout a journey that’s carrying
out simply two concurrent experiments. We converse extra on unintended complexity later on this part.
The take a look at
pyramid is a well-known heuristic that recommends a
relationship between the price of a take a look at (upkeep and
writing) and its amount within the system. Our consumer had saved
to the take a look at pyramid and we discovered unit, subcutaneous and
journey-centric UI-driving exams after we examined their
code. The answer subsequently was to proceed to observe the
sample: Increasing the variety of exams throughout all layers and
additionally extending the suite of journey exams to include the
leaping out and in of our embedded Strangler Fig app. However
there was a possible drawback, possession. We realized
that it will be unreasonable to tie the success of one other
staff’s construct to code they didn’t write or had been answerable for.
We subsequently proposed the next take a look at technique throughout
groups:
| Check Sort | Native | React Native |
|---|---|---|
| Unit | X | X |
| Subcutaneous | X | X |
| Legacy Journey | X | |
| e2e Micro-app Journey | X | |
| Contract exams for interactions with ‘The Bridge’ (journeys with each legacy and micro-app parts) | X | X |
On the final desk row, by contract we merely imply:
If I work together with the bridge interface a specific means, I
count on a selected occasion to fireside
For Native to RN interactions, these contracts act as blueprints
for micro-apps and allow unit testing with mocks. Mocks
simulate the habits of the micro-app, guaranteeing it makes use of
the required context appropriately.
The opposite means round (RN to Native) was related. We recognized
the Native performance we wished to name by way of the
Bridge. RN then supplied us with an object referred to as
NativeModules which, when mocked, allowed us to claim
in opposition to the ensuing context.
Defining these boundaries of accountability meant that we might
restrict the ‘regression-related’ cognitive load on groups by way of
‘hand-off’ factors with out compromising on general app take a look at
protection.
This technique was largely effectively acquired by each the native and
non-native groups. The place we did run into friction was the
complexity behind the implementation of the contract exams
throughout the bridge. The staff operating the legacy software
merely didn’t have the bandwidth to grasp and write a
new class of exams. As a compromise, in the course of
the PoC, all contract exams had been written by the React Native
staff. From this we discovered that any interstitial state
required regarded as paid to the developer expertise. In
our case, merely layering complexity to realize our objectives
was solely a part of the issue to be solved.
Creating the Experiment
Bringing all the things collectively to kind an experiment was the final
hurdle we needed to overcome. We wanted a method to have the ability to
show measurable success from two totally different
experiences and now have a capability to shortly backout and
revert a change if issues had been going fallacious.
The group had an present integration with an
experimentation device, so out of ease, we selected it as our
device for metric seize and experiment measurement. For experiment
consumer choice, we determined system stage consumer choice (IMEI
quantity) can be extra consultant. This was because of the
potential for a number of system utilization throughout a single account
skewing the outcomes.
We additionally utilized the function
flagging part of the experimentation device to permit us to ‘flip off’ the experiment (revert to
native app solely) with out the necessity for a launch; drastically
lowering the time taken to get better ought to any outage happen.
Outcomes
We’ve instructed the story of how we carried out the Strangler Fig sample
in opposition to a big, complicated legacy software, however how
profitable was it with our consumer?
Our consumer selected a website/journey that mapped to an present smaller
micro-app to be the primary that will be incrementally changed
contained in the legacy software. This was as a result of the micro-app was
tried and examined in different purposes across the enterprise and was
generic sufficient that it could possibly be simply ‘white labeled’ by our staff.
Following the success of the primary micro-app integration, a second,
bigger micro-app was then implanted to show the sample
was extensible. These had been the outcomes:
Time to First Worth
Getting a product in entrance of customers early allows worth to be
realized cumulatively over time and precise consumer suggestions to be collected
and iterated upon. An extended time to worth will increase the influence of
altering necessities and delays the belief of advantages. The primary
metric involved time to first worth for our new expertise. This determine
is derived from the time it took to create the Strangler Fig framework
inside the prevailing legacy app and all regression/integration actions
across the first micro-app.
By comparability, our consumer had been quoted
round two years for a complete software rewrite. Within the case of the Strangler Fig, It took round 1 month to implant the micro-app construction into the prevailing
software, 3 months to construct the primary micro-app, and 5 months for the
second. Therefore, from a clean web page, it will take 4 months to yield first
worth (implantation plus first app). Whereas that is the fairest solution to
make the comparability, in reality the consumer noticed first worth a lot faster.
It’s because each micro-apps had already been built to be used in
separate cellular purposes. So the time to first worth on this case
was solely the implantation time of 1 month.
Cycle Time
Our second measurement is Cycle Time. It represents the time to
make a change contained in the micro-app code and contains time taken for
regression with the Strangler Fig app. It excludes pushing an app
to the shop – a variable size course of that app kind has no bearing on.
Within the case of our legacy app, we calculated cycle time because the period
it took to make and regression take a look at a change within the present native code
base.
The metric is beneficial as a result of its uplift represents a shift in
organizational threat aversion in opposition to the product; modifications prior to now
being exhaustively examined because of the potential for unrelated aspect
results and outages. As our present micro app was a wholly
encapsulated area, we knew that the overwhelming majority of modifications can be
owned by the micro-app staff and subsequently totally testable contained in the micro-app
itself. Any exceptions the place the bridge was invoked (e.g. native
performance requested) could possibly be mapped to contract exams on the
boundaries.
| App Sort | Median Cycle Time (over 30 days) |
|---|---|
| Micro-App 1 | 9 days |
| Micro-App 2 | 10 days |
| Legacy App | 20 days |
The
outcomes above present a major uplift in
velocity to make code modifications inside
encapsulated area boundaries (micro-apps)
when in comparison with a coupled monolithic
app construction.
Limitations and Recognized Drawbacks
To this point we’ve largely highlighted the advantages of a Strangler Fig
strategy to legacy cellular App displacement. Nonetheless, there are some
vital limitations to this sample that ought to be taken under consideration
earlier than selecting to copy our experiment. We acknowledge that our use
of the
sample originated from a proof of idea: A request from a consumer
unwilling to just accept that there was just one choice to exchange their legacy
software. Whereas the information we see up to now is encouraging when it comes to
cumulative worth supply and enhancements in cycle time, it’s laborious to
ignore an absence of information from the right side of the development process. Earlier than
recommending this as an choice for legacy substitute, we would want to
see information on app resilience akin to time to restore service and quantity/severity of outages. Pondering additional forward, we additionally acknowledge the
limitations of solely making use of the sample to 2 of the various domains the
consumer’s app was composed of. It stays to be seen if there are any
complexity issues created when extra domains are launched to the
interstitial app state.
Abstract
Recapping, we began this text by explaining why, as cellular
apps have grown in complexity, incremental legacy
modernization has turn into extra engaging. From there, we
launched the Strangler Fig sample for Cell
Functions. We confirmed the assorted levels within the course of
from preliminary function deployment by way of to eventual full
substitute. We examined a few of the extra complicated
implementation challenges intimately. We demonstrated how our
Strangler Fig was implanted into the legacy app. We dove deeper into the idea by analyzing the React
Native Bridge as a method to facilitate communication between
previous and new. We mentioned how the dealing with of delicate information befell. We additionally confirmed how efficient regression
take a look at protection might occur when confronted with a number of impartial groups. Lastly, we touched on how leveraging experimentation in opposition to the sample, was helpful in an incremental supply atmosphere.
We found encouraging ends in that our PoC was capable of
considerably shorten the trail to first worth when in comparison with the estimated time for a full app rewrite.
Our use of modular micro-apps additionally confirmed a 50% enchancment within the median cycle time when
in contrast in opposition to that of the prevailing
legacy cellular app. With that being stated, we acknowledge the
limitations of our standing as a PoC and the unintended complexity incurred that wanted managing. We
counsel additional exploration of the resiliency and scalability of the
sample earlier than it’s a dependable various
to the normal strategies of cellular app modernization.
To sum up, we consider that it’s innevitable cellular apps will proceed to
improve in scope and complexity.
We additionally suppose that attitudes round threat mitigation and quicker worth
supply will turn into extra commonplace
when contemplating modernization of a sufficiently complicated app. To
some extent, this calls for a brand new strategy, maybe that which was
proposed on this article. Nonetheless, regardless of the successes we’ve
seen, this shouldn’t be overplayed
as greater than a device as a part of a wider ‘legacy modernization
toolbelt’. These seeking to replicate
ought to perceive before everything that Legacy Modernization,
no matter know-how, is a multifaceted
drawback that calls for vital evaluation and alignment. Placing in
the funding upfront, won’t solely assist you choose
the proper device to your scenario, however be certain that your app is
higher aligned to the shoppers it serves
and the issues it solves.
