On this article we goal to point out why taking an incremental strategy to
legacy cellular software modernization might be preferable to the classical
‘rewrite from scratch’ methodology. Thoughtworks has the advantage of working with
giant enterprise shoppers which can be depending on their in-house cellular
functions for his or her core enterprise. We see lots of them asking their
functions 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 based mostly in Area Pushed Design and hinge on the
software of the Strangler Fig sample. Whereas these ideas are removed from
new, we imagine that their utilization in cellular functions are novel. We really feel
that regardless of incurring a bigger non permanent 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 improvement
whereas gaining a platform to decrease threat and drive incremental worth
supply.
We focus on how this works in concept, 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 all Thoughtworks’ enterprise
shoppers. We spotlight how the sample enabled our shopper to quickly construct,
take a look at and productionize a modernized subset of area functionalities inside
an current legacy software.
We transfer on to judge the effectiveness of the trial by highlighting the enterprise
dealing with advantages resembling a signficantly quicker time to worth and a 50% lowered median cycle
time. We additionally contact on different anticipated advantages that ought to be used to
measure the success of this technique.
The Downside with Cellular Legacy Modernization
As functions age and develop, they have an inclination to deteriorate each in high quality
and efficiency. Options take longer to get to market whereas outages
and rolled again releases change into extra extreme and frequent. There’s a
nuanced complexity to be understood concerning the the explanation why this
happens each on the code and organizational degree.
To summarize although, in some unspecified time in the future, an
group will develop uninterested in the poor outcomes from their
software program and begin the method of legacy alternative. The choice
to switch could also be made based mostly on a number of components, together with (however not restricted to)
price/profit evaluation, threat evaluation, or alternative price. Ultimately a legacy modernization technique shall be chosen.
This shall 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
alternative/displacement than an easier, much less enterprise vital one.
Within the case of cellular software modernization, these selections have
in current reminiscence been moderately clear minimize. A cellular software was
typically designed to do a person thing- Apple’s “There’s an app for
that” nonetheless rings out loud and clear in folks’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 it is advisable do
one thing, write an app to do it. If it is advisable 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 seen I
had a number of apps from the producer of my automotive; an older one and a more moderen
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 units, and at the very 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 sophisticated,
that it couldn’t be torn down, break up out or began from scratch once more.
However what occurs when this isn’t the case? Certainly not all apps are
created equal? Many imagine that the cellular expertise of the longer term
shall 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” functions like
‘WeChat’ and ‘AliPay’- we see the cellular system and its working
system as extra of a automobile to permit the working of those gigantic
items of software program. Feedback from trade point out a realization
that the West
will not be fairly as far alongside as China on 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 as we speak one is offered with “Movies”
and “Shorts”, a information feed, controllable classes, subscriptions,
to not point out a content material modifying and publishing studio. Equally
with the Uber app, the person is requested in the event that they need 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 definitely enriched a person’s expertise however in addition they make the
conventional construct, use, rebuild method rather more troublesome.
This problem might be defined by contemplating a number of the current
frequent issues of cellular software improvement:
- Huge View Controllers/Actions/Fragments
- Direct manipulation of UI parts
- Platform particular code
- Poor Separation of Considerations
- Restricted Testability
With self-discipline, these issues might be managed early on. Nonetheless, with
a big software that has grown chaotically inline with the enterprise it
helps, incremental change shall be troublesome regardless. The answer then, as
earlier than, is to construct new and launch all of sudden. However what in the event you solely need
so as to add a brand new function, or modernize an current 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 in the event you’re completely happy together with your
app retailer evaluations and don’t need to threat impacting them?
Taking an incremental strategy to app alternative then is the important thing to
avoiding the pitfalls related to ‘large bang releases’. The Strangler
Fig sample is commonly used to rebuild a legacy software in
place: a brand new system is step by step created across the edges of an previous
one via frequent releases. This sample is well-known, however
not broadly utilized in a cellular context. We imagine the rationale 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 downside into
smaller, deliverable components:
- Perceive the outcomes you need to obtain
- Resolve the way to 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 current 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 shopper that
impressed the writing of this text – a globally distributed enterprise
with a longtime retail group that had embraced cellular
functions for a few years. Our shopper had realized the advantages an
app introduced to supply a self-service expertise for his or her
merchandise. They’d rapidly expanded and developed their app domains to permit thousands and thousands
of shoppers 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 functions in its smaller
sub-brands. Responding to an absence of reuse/important duplication of
efforts, excessive
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 frequent to
the group (e.g. ‘login/registration/auth’ or ‘grocery buying’)
throughout completely different manufacturers and territories, in a fraction of the time it
would have taken to put in writing all of them individually.
The diagram above is a simplified illustration of the modular
structure the group had efficiently applied. React
Native was used resulting from its capacity to thoroughly encapsulate a
area’s bounded context inside an importable element. Every
element 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 offered the app particular configuration
and theming to the person micro-apps. This ‘full slice’ of
performance has some great benefits of each permitting re-use and
lowering complexity by abstracting software domains to micro-apps
managed by particular person groups. We communicate 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
quite a few smaller sub-brands that served related merchandise in different
territories. With the modular structure sample tried and examined, the
group needed to focus efforts on its ‘home-territory’ cellular
software (serving its foremost model). Their foremost cellular app was a lot
bigger by way of function richness, income and person volumes to that of
the sub manufacturers. The app had been gaining options and customers over many
years of product improvement. This regular however important progress had
introduced success by way of how well-regarded their software program was on each
Google and Apple shops. Nonetheless, it additionally began to point out 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 needed an software that might evolve as
quick as their merchandise did. Their lengthy launch cycle was associated to threat
aversion: Any outage within the software was a critical lack of income to
the group and likewise triggered their clients misery because of the
important nature of the merchandise they offered. Modifications had been at all times examined
exhaustively earlier than being put reside.
The group first thought-about a rewrite of all the software
and had been shocked by the associated fee and period of such a challenge. The potential
detrimental reception of a ‘large bang’ new launch to their app retailer
clients additionally triggered considerations within the ranges of threat they might settle for.
Solutions of alpha and beta person 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 larger price and threat.
Thoughtworks steered an preliminary proof of idea that constructed on the
successes of the reusability already seen with a modular
structure. We addressed the group’s large bang threat aversion
by suggesting the Strangler
Fig sample to incrementally exchange particular person domains. By
leveraging each methods 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 shoppers a lot sooner
with much less duplication than in a full rewrite. Our focus was not on
delivering essentially the most lovely or cohesive full app expertise (-not
fairly but anyway). It was about acquiring confidence each within the
stability of the iterative alternative sample and likewise in how properly
the brand new product was being acquired. These items of data
allowed the group to make extra knowledgeable product selections
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 truly 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 (Resolve the way to break the issue into
smaller components). We centered our efforts on discovering navigation entry factors
to domains, we referred to as them our ‘factors of interception’. These acquainted
with cellular software improvement will know that navigation is mostly
a properly encapsulated concern, which means that we might be assured that we
might at all times direct our customers to the expertise of our selecting.
As soon as we recognized our ‘factors of interception’, we chosen a site
for incremental alternative/retirement. Within the instance above we deal with
the Grocery area throughout the current 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
total React Native software inside the prevailing legacy software.
The crew took the chance to observe the nice modularity practices that
the framework encourages and constructed Grocery as an encapsulated element. This
meant that as we added extra domains to our Strangler Fig Embedded
Utility, we might management their enablement on a person degree.
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 via the identical
interfaces the legacy cellular software did. Translation between each
monolith and micro-app occurred in each instructions as vital. This
allowed the brand new module’s frontend to not be constrained by the legacy API
because it developed.
We continued the within out alternative of the previous software by
repeating the method once more on the subsequent 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 ultimately only a shell
containing the brand new React Native software. This then would permit the elimination of the
previous native software completely, leaving the brand new one instead. 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 large bang launch had been negated.
Diving Deeper…
Thus far we’ve offered a really broad set of diagrams to
illustrate our Cellular Strangler Fig idea. Nonetheless, there are
nonetheless many
excellent implementation-focused questions with a purpose to take concept
into
observe.
Implanting the Strangler Fig
A very good begin could be, how did we summary the complexity of
constructing each native and non-native codebases?
Beginning with the repository construction, we turned our authentic native
software construction inside out. By inverting the management
of the native software to a React Native (RN) software
we prevented important duplication related to nesting
our RN listing twice inside every cellular working system’s
folder. In truth, 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 authentic directories, solely this time it was inside a single
repository. The diagram beneath is a generalized illustration (that’s,
relevant to each iOS and Android) of the present pipeline from the
Shopper 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 value 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 objective is to function the message queue for
directions like rendering views, calling native capabilities,
occasion handlers, passing values and so on. Examples of
properties handed throughout the bridge can be isCartOpen
or sessionDuration. Whereas an instance of a bridge
operate name could 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 once we
described our app by way of 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 got 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 got extra confidence in one in all our new journeys
than one other we’re afforded the choice to divert a bigger
proportion of site 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 forwards and backwards throughout experiences.
The flexibility to move info allowed us to protect any
quick state or motion from the UI that wanted to
persevere throughout experiences. This was significantly 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 once we crossed the bridge.
Dealing with Delicate Information
Thus far we’ve mentioned shifting between legacy and new codebases as
atomic entities. We’ve touched on how native state might be
shared throughout the bridge, however what about extra delicate
knowledge? Having not too long ago changed their login and registration (auth)
course of of their different customer-facing React Native apps
with a modular, configurable, model agnostic one, the shopper
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 methods already mentioned 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 would have liked 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 knowledge to the Android
EncryptedSharedPreferences or iOS Keychain after a
profitable login. Because of the versatile construction of the info
contained in the keystore, it allowed us to seamlessly share the
(re)authentication course of no matter whether or not
the person was within the native or non-native expertise. It additionally
gave us a sample for the safe sharing of any delicate
knowledge 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, completely different groups working throughout the similar app) whether or not a change made affected the
total performance of the system. The embedded app
sample described above presents a singular 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 communicate extra on unintentional 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 shopper had stored
to the take a look at pyramid and we discovered unit, subcutaneous and
journey-centric UI-driving checks once we examined their
code. The answer subsequently was to proceed to observe the
sample: Increasing the variety of checks throughout all layers and
additionally extending the suite of journey checks to include the
leaping out and in of our embedded Strangler Fig app. However
there was a possible downside, possession. We realized
that it will be unreasonable to tie the success of one other
crew’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 checks for interactions with ‘The Bridge’ (journeys with each legacy and micro-app elements) | X | X |
On the final desk row, by contract we merely imply:
If I work together with the bridge interface a selected means, I
anticipate a particular occasion to fireplace
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, making certain 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 via the
Bridge. RN then offered 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 duty meant that we might
restrict the ‘regression-related’ cognitive load on groups via
‘hand-off’ factors with out compromising on total app take a look at
protection.
This technique was largely properly 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 checks
throughout the bridge. The crew working the legacy software
merely didn’t have the bandwidth to know and write a
new class of checks. As a compromise, at some stage in
the PoC, all contract checks had been written by the React Native
crew. From this we realized that any interstitial state
required considered paid to the developer expertise. In
our case, merely layering complexity to attain our objectives
was solely a part of the issue to be solved.
Creating the Experiment
Bringing every thing collectively to kind an experiment was the final
hurdle we needed to overcome. We would have liked a method to have the ability to
show measurable success from two completely different
experiences and still have a capability to rapidly backout and
revert a change if issues had been going incorrect.
The group had an current integration with an
experimentation device, so out of ease, we selected it as our
device for metric seize and experiment measurement. For experiment
person choice, we determined system degree person 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 element of the experimentation device to permit us to ‘flip off’ the experiment (revert to
native app solely) with out the necessity for a launch; vastly
lowering the time taken to get better ought to any outage happen.
Outcomes
We’ve instructed the story of how we applied the Strangler Fig sample
in opposition to a big, advanced legacy software, however how
profitable was it with our shopper?
Our shopper selected a site/journey that mapped to an current 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 functions across the enterprise and was
generic sufficient that it might be simply ‘white labeled’ by our crew.
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 person suggestions to be collected
and iterated upon. An extended time to worth will increase the impression 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 shopper 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 strategy to
make the comparability, actually the shopper noticed first worth a lot faster.
It is because each micro-apps had already been constructed to be used in
separate cellular functions. 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 current native code
base.
The metric is helpful as a result of its uplift represents a shift in
organizational threat aversion in opposition to the product; adjustments previously
being exhaustively examined because of the potential for unrelated aspect
results and outages. As our current micro app was a wholly
encapsulated area, we knew that the overwhelming majority of adjustments can be
owned by the micro-app crew and subsequently absolutely testable contained in the micro-app
itself. Any exceptions the place the bridge was invoked (e.g. native
performance requested) might be mapped to contract checks 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 adjustments inside
encapsulated area boundaries (micro-apps)
when in comparison with a coupled monolithic
app construction.
Limitations and Recognized Drawbacks
Thus far we’ve largely highlighted the advantages of a Strangler Fig
strategy to legacy cellular App displacement. Nonetheless, there are some
important 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 shopper
unwilling to just accept that there was just one possibility to switch their legacy
software. Whereas the info we see so far is encouraging by way of
cumulative worth supply and enhancements in cycle time, it’s laborious to
ignore an absence of knowledge from the proper aspect of the event course of. Earlier than
recommending this as an possibility for legacy alternative, we would wish to
see knowledge on app resilience resembling time to revive 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
shopper’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 change into extra enticing. From there, we
launched the Strangler Fig sample for Cellular
Purposes. We confirmed the assorted levels within the course of
from preliminary function deployment via to eventual full
alternative. We examined a number of the extra advanced
implementation challenges intimately. We demonstrated how our
Strangler Fig was implanted into the legacy app. We dove deeper into the idea by inspecting the React
Native Bridge as a method to facilitate communication between
previous and new. We mentioned how the dealing with of delicate knowledge happened. 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 surroundings.
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 unintentional 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 imagine 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 change into extra commonplace
when contemplating modernization of a sufficiently advanced 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 got
seen, this shouldn’t be overplayed
as greater than a device as a part of a wider ‘legacy modernization
toolbelt’. These trying to replicate
ought to perceive initially that Legacy Modernization,
no matter know-how, is a multifaceted
downside that calls for important evaluation and alignment. Placing in
the funding upfront, is not going to solely assist you choose
the proper device on your scenario, however make sure that your app is
higher aligned to the shoppers it serves
and the issues it solves.
