Legacy Modernization meets GenAI

Legacy Modernization meets GenAI


For the reason that launch of ChatGPT in November 2022, the GenAI
panorama has undergone fast cycles of experimentation, enchancment, and
adoption throughout a variety of use instances. Utilized to the software program
engineering business, GenAI assistants primarily assist engineers write code
quicker by offering autocomplete ideas and producing code snippets
primarily based on pure language descriptions. This strategy is used for each
producing and testing code. Whereas we recognise the great potential of
utilizing GenAI for ahead engineering, we additionally acknowledge the numerous
problem of coping with the complexities of legacy programs, along with
the truth that builders spend much more time studying code than writing it.

By modernizing quite a few legacy programs for our purchasers, we now have discovered that an evolutionary strategy makes
legacy displacement each safer and more practical at attaining its worth targets. This methodology not solely reduces the
dangers of modernizing key enterprise programs but additionally permits us to generate worth early and incorporate frequent
suggestions by progressively releasing new software program all through the method. Regardless of the optimistic outcomes we now have seen
from this strategy over a “Massive Bang” cutover, the price/time/worth equation for modernizing giant programs is usually
prohibitive. We imagine GenAI can flip this example round.

For our half, we now have been experimenting during the last 18 months with
LLMs to sort out the challenges related to the
modernization of legacy programs. Throughout this time, we now have developed three
generations of CodeConcise, an inner modernization
accelerator at Thoughtworks . The motivation for
constructing CodeConcise stemmed from our commentary that the modernization
challenges confronted by our purchasers are related. Our aim is for this
accelerator to grow to be our smart default in
legacy modernization, enhancing our modernization worth stream and enabling
us to comprehend the advantages for our purchasers extra effectively.

We intend to make use of this text to share our expertise making use of GenAI for Modernization. Whereas a lot of the
content material focuses on CodeConcise, that is just because we now have hands-on expertise
with it. We don’t counsel that CodeConcise or its strategy is the one technique to apply GenAI efficiently for
modernization. As we proceed to experiment with CodeConcise and different instruments, we
will share our insights and learnings with the group.

GenAI period: A timeline of key occasions

One major purpose for the
present wave of hype and pleasure round GenAI is the
versatility and excessive efficiency of general-purpose LLMs. Every new era of those fashions has persistently
proven enhancements in pure language comprehension, inference, and response
high quality. We’re seeing a lot of organizations leveraging these highly effective
fashions to satisfy their particular wants. Moreover, the introduction of
multimodal AIs, equivalent to text-to-image generative fashions like DALL-E, alongside
with AI fashions able to video and audio comprehension and era,
has additional expanded the applicability of GenAIs. Furthermore, the
newest AI fashions can retrieve new data from real-time sources,
past what’s included of their coaching datasets, additional broadening
their scope and utility.

Since then, we now have noticed the emergence of latest software program merchandise designed
with GenAI at their core. In different instances, current merchandise have grow to be
GenAI-enabled by incorporating new options beforehand unavailable. These
merchandise sometimes make the most of basic function LLMs, however these quickly hit limitations when their use case goes past
prompting the LLM to generate responses purely primarily based on the information it has been educated with (text-to-text
transformations). For example, in case your use case requires an LLM to grasp and
entry your group’s knowledge, essentially the most economically viable answer typically
includes implementing a Retrieval-Augmented Era (RAG) strategy.
Alternatively, or together with RAG, fine-tuning a general-purpose mannequin is likely to be applicable,
particularly if you happen to want the mannequin to deal with advanced guidelines in a specialised
area, or if regulatory necessities necessitate exact management over the
mannequin’s outputs.

The widespread emergence of GenAI-powered merchandise might be partly
attributed to the provision of quite a few instruments and improvement
frameworks. These instruments have democratized GenAI, offering abstractions
over the complexities of LLM-powered workflows and enabling groups to run
fast experiments in sandbox environments with out requiring AI technical
experience. Nonetheless, warning should be exercised in these comparatively early
days to not fall into traps of comfort with frameworks to which
Thoughtworks’ latest know-how radar
attests
.

Issues that make modernization costly

After we started exploring the usage of “GenAI for Modernization”, we
centered on issues that we knew we might face many times – issues
we knew had been those inflicting modernization to be time or value
prohibitive.

  • How can we perceive the prevailing implementation particulars of a system?
  • How can we perceive its design?
  • How can we collect data about it with out having a human skilled obtainable
    to information us?
  • Can we assist with idiomatic translation of code at scale to our desired tech
    stack? How?
  • How can we decrease dangers from modernization by enhancing and including
    automated exams as a security internet?
  • Can we extract from the codebase the domains, subdomains, and
    capabilities?
  • How can we offer higher security nets in order that variations in habits
    between previous programs and new programs are clear and intentional? How can we allow
    cut-overs to be as headache free as attainable?

Not all of those questions could also be related in each modernization
effort. We have now intentionally channeled our issues from essentially the most
difficult modernization situations: Mainframes. These are a few of the
most vital legacy programs we encounter, each by way of measurement and
complexity. If we will clear up these questions on this situation, then there
will definitely be fruit born for different know-how stacks.

The Structure of CodeConcise

Determine 1: The conceptual strategy of CodeConcise.

CodeConcise is impressed by the Code-as-data
idea
, the place code is
handled and analyzed in methods historically reserved for knowledge. This implies
we’re not treating code simply as textual content, however by way of using language
particular parsers, we will extract its intrinsic construction, and map the
relationships between entities within the code. That is finished by parsing the
code right into a forest of Summary Syntax Timber (ASTs), that are then
saved in a graph database.

Determine 2: An ingestion pipeline in CodeConcise.

Edges between nodes are then established, for instance an edge is likely to be saying
“the code on this node transfers management to the code in that node”. This course of
doesn’t solely permit us to grasp how one file within the codebase may relate
to a different, however we additionally extract at a a lot granular degree, for instance, which
conditional department of the code in a single file transfers management to code within the
different file. The flexibility to traverse the codebase at such a degree of granularity
is especially necessary because it reduces noise (i.e. pointless code) from the
context offered to LLMs, particularly related for recordsdata that don’t include
extremely cohesive code. Primarily, there are two advantages we observe from this
noise discount. First, the LLM is extra prone to keep focussed on the immediate.
Second, we use the restricted area within the context window in an environment friendly method so we
can match extra data into one single immediate. Successfully, this permits the
LLM to research code in a method that’s not restricted by how the code is organized in
the primary place by builders. We discuss with this deterministic course of because the ingestion pipeline.

Determine 3: A simplified illustration of how a data graph may appear to be for a Java codebase.

Subsequently, a comprehension pipeline traverses the graph utilizing a number of
algorithms, equivalent to Depth-first Search with
backtracking
in post-order
traversal, to complement the graph with LLM-generated explanations at varied depths
(e.g. strategies, lessons, packages). Whereas some approaches at this stage are
widespread throughout legacy tech stacks, we now have additionally engineered prompts in our
comprehension pipeline tailor-made to particular languages or frameworks. As we started
utilizing CodeConcise with actual, manufacturing shopper code, we recognised the necessity to
maintain the comprehension pipeline extensible. This ensures we will extract the
data most beneficial to our customers, contemplating their particular area context.
For instance, at one shopper, we found {that a} question to a particular database
desk applied in code could be higher understood by Enterprise Analysts if
described utilizing our shopper’s enterprise terminology. That is significantly related
when there’s not a Ubiquitous
Language
shared between
technical and enterprise groups. Whereas the (enriched) data graph is the primary
product of the comprehension pipeline, it isn’t the one priceless one. Some
enrichments produced through the pipeline, equivalent to robotically generated
documentation in regards to the system, are priceless on their very own. When offered
on to customers, these enrichments can complement or fill gaps in current
programs documentation, if one exists.

Determine 4: A comprehension pipeline in CodeConcise.

Neo4j, our graph database of alternative, holds the (enriched) Data Graph.
This DBMS options vector search capabilities, enabling us to combine the
Data Graph into the frontend utility implementing RAG. This strategy
offers the LLM with a a lot richer context by leveraging the graph’s construction,
permitting it to traverse neighboring nodes and entry LLM-generated explanations
at varied ranges of abstraction. In different phrases, the retrieval part of RAG
pulls nodes related to the consumer’s immediate, whereas the LLM additional traverses the
graph to collect extra data from their neighboring nodes. For example,
when searching for data related to a question about “how does authorization
work when viewing card particulars?” the index could solely present again outcomes that
explicitly cope with validating consumer roles, and the direct code that does so.
Nonetheless, with each behavioral and structural edges within the graph, we will additionally
embrace related data in known as strategies, the encircling bundle of code,
and within the knowledge buildings which were handed into the code when offering
context to the LLM, thus frightening a greater reply. The next is an instance
of an enriched data graph for AWS Card
Demo
,
the place blue and inexperienced nodes are the outputs of the enrichments executed within the
comprehension pipeline.

Determine 5: An (enriched) data graph for AWS Card Demo.

The relevance of the context offered by additional traversing the graph
finally depends upon the standards used to assemble and enrich the graph within the
first place. There isn’t any one-size-fits-all answer for this; it can rely on
the particular context, the insights one goals to extract from their code, and,
finally, on the rules and approaches that the event groups adopted
when developing the answer’s codebase. For example, heavy use of
inheritance buildings may require extra emphasis on INHERITS_FROM edges vs
COMPOSED_OF edges in a codebase that favors composition.

For additional particulars on the CodeConcise answer mannequin, and insights into the
progressive studying we had by way of the three iterations of the accelerator, we
will quickly be publishing one other article: Code comprehension experiments with
LLMs.

Within the subsequent sections, we delve deeper into particular modernization
challenges that, if solved utilizing GenAI, may considerably impression the price,
worth, and time for modernization – components that usually discourage us from making
the choice to modernize now. In some instances, we now have begun exploring internally
how GenAI may deal with challenges we now have not but had the chance to
experiment with alongside our purchasers. The place that is the case, our writing is
extra speculative, and we now have highlighted these cases accordingly.

Reverse engineering: drawing out low-level necessities

When endeavor a legacy modernization journey and following a path
like Rewrite or Exchange, we now have realized that, with a view to draw a
complete record of necessities for our goal system, we have to
study the supply code of the legacy system and carry out reverse
engineering. These will information your ahead engineering groups. Not all
these necessities will essentially be included into the goal
system, particularly for programs developed over a few years, a few of which
could not be related in right this moment’s enterprise and market context.
Nonetheless, it’s essential to grasp current habits to make knowledgeable
selections about what to retain, discard, and introduce in your new
system.

The method of reverse engineering a legacy codebase might be time
consuming and requires experience from each technical and enterprise
folks. Allow us to contemplate under a few of the actions we carry out to achieve
a complete low-level understanding of the necessities, together with
how GenAI can assist improve the method.

Guide code opinions

Encompassing each static and dynamic code evaluation. Static
evaluation includes reviewing the supply code immediately, typically
aided by particular instruments for a given technical stack. These intention to
extract insights equivalent to dependency diagrams, CRUD (Create Learn
Replace Delete) reviews for the persistence layer, and low-level
program flowcharts. Dynamic code evaluation, then again,
focuses on the runtime habits of the code. It’s significantly
helpful when a bit of the code might be executed in a managed
setting to watch its habits. Analyzing logs produced throughout
runtime may present priceless insights into the system’s
habits and its parts. GenAI can considerably improve
the understanding and clarification of code by way of code opinions,
particularly for engineers unfamiliar with a selected tech stack,
which is usually the case with legacy programs. We imagine this
functionality is invaluable to engineering groups, because it reduces the
typically inevitable dependency on a restricted variety of consultants in a
particular stack. At one shopper, we now have leveraged CodeConcise,
using an LLM to extract low-level necessities from the code. We
have prolonged the comprehension pipeline to supply static reviews
containing the knowledge Enterprise Analysts (BAs) wanted to
successfully derive necessities from the code, demonstrating how
GenAI can empower non-technical folks to be concerned in
this particular use case.

Abstracted program flowcharts

Low-level program flowcharts can obscure the general intent of
the code and overwhelm BAs with extreme technical particulars.
Subsequently, collaboration between reverse engineers and Topic
Matter Consultants (SMEs) is essential. This collaboration goals to create
abstracted variations of program flowcharts that protect the
important flows and intentions of the code. These visible artifacts
assist BAs in harvesting necessities for ahead engineering. We have now
learnt with our shopper that we may make use of GenAI to supply
summary flowcharts for every module within the system. Whereas it might be
cheaper to manually produce an summary flowchart at a system degree,
doing so for every module(~10,000 traces of code, with a complete of 1500
modules) could be very inefficient. With GenAI, we had been in a position to
present BAs with visible abstractions that exposed the intentions of
the code, whereas eradicating a lot of the technical jargon.

SME validation

SMEs are consulted at a number of levels through the reverse
engineering course of by each builders and BAs. Their mixed
technical and enterprise experience is used to validate the
understanding of particular components of the system and the artifacts
produced through the course of, in addition to to make clear any excellent
queries. Their enterprise and technical experience, developed over many
years, makes them a scarce useful resource inside organizations. Typically,
they’re stretched too skinny throughout a number of groups simply to “maintain
the lights on”
. This presents a chance for GenAI
to cut back dependencies on SMEs. At our shopper, we experimented with
the chatbot featured in CodeConcise, which permits BAs to make clear
uncertainties or request extra data. This chatbot, as
beforehand described, leverages LLM and Data Graph applied sciences
to supply solutions just like these an SME would supply, serving to to
mitigate the time constraints BAs face when working with them.

Thoughtworks labored with the shopper talked about earlier to discover methods to
speed up the reverse engineering of a giant legacy codebase written in COBOL/
IDMS. To attain this, we prolonged CodeConcise to help the shopper’s tech
stack and developed a proof of idea (PoC) using the accelerator within the
method described above. Earlier than the PoC, reverse engineering 10,000 traces of code
sometimes took 6 weeks (2 FTEs working for 4 weeks, plus wait time and an SME
assessment). On the finish of the PoC, we estimated that our answer may cut back this
by two-thirds, from 6 weeks to 2 weeks for a module. This interprets to a
potential saving of 240 FTE years for the whole mainframe modernization
program.

Excessive-level, summary clarification of a system

We have now skilled that LLMs can assist us perceive low-level
necessities extra shortly. The subsequent query is whether or not they may
assist us with high-level necessities. At this degree, there’s a lot
data to absorb and it’s robust to digest all of it. To sort out this,
we create psychological fashions which function abstractions that present a
conceptual, manageable, and understandable view of the purposes we
are wanting into. Often, these fashions exist solely in folks’s heads.
Our strategy includes working carefully with consultants, each technical and
enterprise focussed, early on within the undertaking. We maintain workshops, equivalent to
Occasion
Storming

from Area-driven Design, to extract SMEs’ psychological fashions and retailer them
on digital boards for visibility, steady evolution, and
collaboration. These fashions include a website language understood by each
enterprise and technical folks, fostering a shared understanding of a
advanced area amongst all crew members. At the next degree of abstraction,
these fashions might also describe integrations with exterior programs, which
might be both inner or exterior to the group.

It’s turning into evident that entry to, and availability of SMEs is
important for understanding advanced legacy programs at an summary degree
in an economical method. Lots of the constraints beforehand
highlighted are due to this fact relevant to this modernization
problem.

Within the period of GenAI, particularly within the modernization area, we’re
seeing good outputs from LLMs when they’re prompted to elucidate a small
subset of legacy code. Now, we wish to discover whether or not LLMs might be as
helpful in explaining a system at the next degree of abstraction.

Our accelerator, CodeConcise, builds upon Code as Knowledge methods by
using the graph illustration of a legacy system codebase to
generate LLM-generated explanations of code and ideas at totally different
ranges of abstraction:

  • Graph traversal technique: We leverage the whole codebase’s
    illustration as a graph and use traversal algorithms to complement the graph with
    LLM-generated explanations at varied depths.
  • Contextual data: Past processing the code and storing it within the
    graph, we’re exploring methods to course of any obtainable system documentation, as
    it typically offers priceless insights into enterprise terminology, processes, and
    guidelines, assuming it’s of fine high quality. By connecting this contextual
    documentation to code nodes on the graph, our speculation is we will improve
    additional the context obtainable to LLMs throughout each upfront code clarification and
    when retrieving data in response to consumer queries.

Finally, the aim is to reinforce CodeConcise’s understanding of the
code with extra summary ideas, enabling its chatbot interface to
reply questions that sometimes require an SME, maintaining in thoughts that
such questions may not be immediately answerable by analyzing the code
alone.

At Thoughtworks, we’re observing optimistic outcomes in each
traversing the graph and producing LLM explanations at varied ranges
of code abstraction. We have now analyzed an open-source COBOL repository,
AWS Card
Demo
,
and efficiently requested high-level questions equivalent to detailing the system
options and consumer interactions. On this event, the codebase included
documentation, which offered extra contextual data for the
LLM. This enabled the LLM to generate higher-quality solutions to our
questions. Moreover, our GenAI-powered crew assistant, Haiven, has
demonstrated at a number of purchasers how contextual details about a
system can allow an LLM to supply solutions tailor-made to
the particular shopper context.

Discovering a functionality map of a system

One of many first issues we do when starting a modernization journey
is catalog current know-how, processes, and the individuals who help
them. Inside this course of, we additionally outline the scope of what’s going to be
modernized. By assembling and agreeing on these components, we will construct a
sturdy enterprise case for the change, develop the know-how and enterprise
roadmaps, and contemplate the organizational implications.
With out having this at hand, there isn’t any technique to decide what wants
to be included, what the plan to realize is, the incremental steps to
take, and after we are finished.

Earlier than GenAI, our groups have been utilizing a lot of
methods to construct this understanding, when it isn’t already current.
These methods vary from Occasion Storming and Course of Mapping by way of
to “following the information” by way of the system, and even focused code
opinions for significantly advanced subdomains. By combining these
approaches, we will assemble a functionality map of our purchasers’
landscapes.
Whereas this will likely seem like a considerable amount of guide effort, these can
be a few of the most beneficial actions because it not solely builds a plan for
the longer term supply, however the considering and collaboration that goes into
making it ensures alignment of the concerned stakeholders, particularly
round what’s going to be included or excluded from the modernization
scope. Additionally, we now have learnt that functionality maps are invaluable after we
take a capability-driven strategy to modernization. This helps modernize
the legacy system incrementally by progressively delivering capabilities in
the goal system, along with designing an structure the place
considerations are cleanly separated.

GenAI adjustments this image loads.

One of the vital highly effective capabilities that GenAI brings is
the power to summarize giant volumes of textual content and different media. We will
use this functionality throughout current documentation that could be current
relating to know-how or processes to extract out, if not the tip
data, then at the least a place to begin for additional conversations.
There are a variety of methods which can be being actively developed and
launched on this space. Specifically, we imagine that
GraphRAG which was lately
launched by Microsoft could possibly be used to extract a degree of data from
these paperwork by way of Graph Algorithm evaluation of the physique of
textual content.
We have now additionally been trialing GenAI excessive of the data graph
that we construct out of the legacy code as talked about earlier by asking what
key capabilities modules have after which clustering and abstracting these
by way of hierarchical summarization. This then serves as a map of
capabilities, expressed succinctly at each a really excessive degree and a
detailed degree, the place every functionality is linked to the supply code
modules the place it’s applied. That is then used to scope and plan for
the modernization in a quicker method. The next is an instance of a
functionality map for a system, together with the supply code modules (small
grey nodes) they’re applied in.

However, we now have learnt to not view this fully LLM-generated
functionality map as mutually unique from the standard strategies of
creating functionality maps described earlier. These conventional approaches
are priceless not just for aligning stakeholders on the scope of
modernization, but additionally as a result of, when a functionality already exists, it
can be utilized to cluster the supply code primarily based on the capabilities
applied. This strategy produces functionality maps that resonate higher
with SMEs by utilizing the group’s Ubiquitous language. Moreover,
evaluating each functionality maps is likely to be a priceless train, certainly one
we sit up for experimenting with, as every may supply insights the
different doesn’t.

Discovering unused / useless / duplicate code

One other a part of gathering data on your modernization efforts
is knowing inside your scope of labor, “what remains to be getting used at
all”, or “the place have we acquired a number of cases of the identical
functionality”.

At present this may be addressed fairly successfully by combining two
approaches: static and dynamic evaluation. Static evaluation can discover unused
methodology calls and statements inside sure scopes of interrogation, for
occasion, discovering unused strategies in a Java class, or discovering unreachable
paragraphs in COBOL. Nonetheless, it’s unable to find out whether or not entire
API endpoints or batch jobs are used or not.

That is the place we use dynamic evaluation which leverages system
observability and different runtime data to find out if these
capabilities are nonetheless in use, or might be dropped from our modernization
backlog.

When seeking to discover duplicate technical capabilities, static
evaluation is essentially the most generally used software as it may well do chunk-by-chunk textual content
similarity checks. Nonetheless, there are main shortcomings when utilized to
even a modest know-how property: we will solely discover code similarities in
the identical language.
We speculate that by leveraging the results of {our capability}
extraction strategy, we will use these know-how agnostic descriptions
of what giant and small abstractions of the code are doing to carry out an
estate-wide evaluation of duplication, which is able to take our future
structure and roadmap planning to the following degree.

Relating to unused code nevertheless, we see little or no use in
making use of GenAI to the issue. Static evaluation instruments within the business for
discovering useless code are very mature, leverage the structured nature of
code and are already at builders’ fingertips, like IntelliJ or Sonar.
Dynamic evaluation from APM instruments is so highly effective there’s little that instruments
like GenAI can add to the extraction of knowledge itself.

Alternatively, these two advanced approaches can yield an enormous
quantity of knowledge to grasp, interrogate and derive perception from. This
could possibly be one space the place GenAI may present a minor acceleration
for discovery of little used code and know-how.
Just like having GenAI discuss with giant reams of product documentation
or specs, we will leverage its data of the static and
dynamic instruments to assist us use them in the best method as an illustration by
suggesting potential queries that may be run over observability stacks.
NewRelic, as an illustration, claims to have built-in LLMs in to its options to
speed up onboarding and error decision; this could possibly be turned to a
modernization benefit too.

Idiomatic translation of tech paradigm

Translation from one programming language to a different just isn’t one thing new. Many of the instruments that do that have
utilized static evaluation methods – utilizing Summary Syntax Timber (ASTs) as intermediaries.

Though these methods and instruments have existed for a very long time, outcomes are sometimes poor when judged by way of
the lens of “would somebody have written it like this if they’d began authoring it right this moment”.

Usually the produced code suffers from:

Poor total Code high quality

Often, the code these instruments produce is syntactically appropriate, however leaves loads to be desired relating to
high quality. A whole lot of this may be attributed to the algorithmic translation strategy that’s used.

Non-idiomatic code

Usually, the code produced doesn’t match idiomatic paradigms of the goal know-how stack.

Poor naming conventions

Naming is nearly as good or unhealthy because it was within the supply language/ tech stack – and even when naming is nice within the
older code, it doesn’t translate nicely to newer code. Think about robotically naming lessons/ objects/ strategies
when translating procedural code that transfers recordsdata to an OO paradigm!

Isolation from open-source libraries/ frameworks

  • Fashionable purposes sometimes use many open-source libraries and frameworks (versus older
    languages) – and producing code at most occasions doesn’t seamlessly do the identical
  • That is much more sophisticated in enterprise settings when organizations are inclined to have inner libraries
    (that instruments won’t be aware of)

Lack of precision in knowledge

Even with primitive sorts languages have totally different precisions – which is prone to result in a loss in
precision.

Loss in relevance of supply code historical past

Many occasions when attempting to grasp code we take a look at how that code developed to that state with git log [or
equivalents for other SCMs] – however now that historical past just isn’t helpful for a similar function

Assuming a corporation embarks on this journey, it can quickly face prolonged testing and verification
cycles to make sure the generated code behaves precisely the identical method as earlier than. This turns into much more difficult
when little to no security internet was in place initially.

Regardless of all of the drawbacks, code conversion approaches proceed to be an choice that draws some organizations
due to their attract as probably the bottom value/ effort answer for leapfrogging from one tech paradigm
to the opposite.

We have now additionally been interested by this and exploring how GenAI can assist enhance the code produced/ generated. It
can not help all of these points, however perhaps it may well assist alleviate at the least the primary three or 4 of them.

From an strategy perspective, we are attempting to use the rules of
Refactoring
to this – basically
determine a method we will safely and incrementally make the bounce from one tech paradigm to a different. This strategy
has already seen some success – two examples that come to thoughts:

Conclusion

Right this moment’s panorama has quite a few alternatives to leverage GenAI to
obtain outcomes that had been beforehand out of attain. Within the software program
business, GenAI is already enjoying a big position in serving to folks
throughout varied roles full their duties extra effectively, and this
impression is predicted to develop. For example, GenAI has produced promising
ends in aiding technical engineers with writing code.

Over the previous a long time, our business has developed considerably, growing patterns, finest practices, and
methodologies that information us in constructing fashionable software program. Nonetheless, one of many greatest challenges we now face is
updating the huge quantity of code that helps key operations each day. These programs are sometimes giant and complicated,
with a number of layers and patches constructed over time, making habits tough to vary. Moreover, there are
typically just a few consultants who absolutely perceive the intricate particulars of how these programs are applied and
function. For these causes, we use an evolutionary strategy to legacy displacement, lowering the dangers concerned
in modernizing these programs and producing worth early. Regardless of this, the price/time/worth equation for
modernizing giant programs is usually prohibitive. On this article, we mentioned methods GenAI might be harnessed to
flip this example round. We are going to proceed experimenting with making use of GenAI to those modernization challenges
and share our insights by way of this text, which we’ll maintain updated. This may embrace sharing what has
labored, what we imagine GenAI may probably clear up, and what, nevertheless, has not succeeded. Moreover, we
will lengthen our accelerator, CodeConcise, with the intention of additional innovating inside the modernization course of to
drive larger worth for our purchasers.

Hopefully, this text highlights the good potential of harnessing
this new know-how, GenAI, to deal with a few of the challenges posed by
legacy programs within the business. Whereas there isn’t any one-size-fits-all
answer to those challenges – every context has its personal distinctive nuances –
there are sometimes similarities that may information our efforts. We additionally hope this
article conjures up others within the business to additional develop experiments
with “GenAI for Modernization” and share their insights with the broader
group.


author avatar
roosho Senior Engineer (Technical Services)
I am Rakib Raihan RooSho, Jack of all IT Trades. You got it right. Good for nothing. I try a lot of things and fail more than that. That's how I learn. Whenever I succeed, I note that in my cookbook. Eventually, that became my blog. 
rooshohttps://www.roosho.com
I am Rakib Raihan RooSho, Jack of all IT Trades. You got it right. Good for nothing. I try a lot of things and fail more than that. That's how I learn. Whenever I succeed, I note that in my cookbook. Eventually, that became my blog. 

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author avatar
roosho Senior Engineer (Technical Services)
I am Rakib Raihan RooSho, Jack of all IT Trades. You got it right. Good for nothing. I try a lot of things and fail more than that. That's how I learn. Whenever I succeed, I note that in my cookbook. Eventually, that became my blog.