Software
development Industry and Development of Software
Gartner predicts that in 2023, software will grow by 12.3% and reach $ 891.39 bn and IT Services will grow by
9.1% and reach $ 1.36v tn. In 20124, the prediction is that the growth will be
12.3% and 9.1% respectively. Software market would be at $ 1 trn and the IT
services market would be at $ 1.5 trn. The IT services segment will continue its growth
trajectory through 2024, largely driven by the infrastructure-as-a-service
market, which is projected to reach over 30% growth this year. For the first
time, price is a key driver of increased spend for cloud services segments,
rather than just increased usage.
According to Nasscom, In FY2023, India’s technology industry revenue
including hardware is estimated to cross $245 Bn (8.4% y-o-y growth), an
addition of $19 Bn over last year. Exports, at $194 Bn, are expected to grow at
9.4% in reported currency terms, and 11.4% in constant currency terms. Domestic
technology sector is expected to reach $51 Bn, growing at 4.9% y-o-y. In rupee
terms, domestic tech revenues is expecting a 13% y-o-y growth on the back of
continued investments by enterprise and the government. The industry continues
to be a net hirer, adding nearly 3 lakh employees, taking the total employee
base to ~5.4 Mn (5.7% y-o-y growth), strengthening its position as the 'Digital
Talent Nation' for the world. From India IT exports were at $ 150 bn in FY20, $
151 bn in FY 21 and $ 176 bn in FY 22.
Software
industry has given a big boost to the Indian economy. The fast development of
industry has helped India to Breakout of Hindu rate of growth. It has accelerated the exports from India.
India as a Global brand started shining after the fast growth of the industry.
When I
joined the industry as a management consultant 1985, the industry size was only USD 100
million. In the software company all the new joinees had to undergo 10 weeks of
software development training. Training was rigorous and it was focussed on job
readiness. I also underwent this
training despite I was in the Management consultancy function.
In
1985, only two companies had 60% market share in the Indian Market. Tata
Burroughs Limited and Tata Consultancy
Services (TCS). TCS was the Pioneer in development of the industry and many
from TCS went to start up software companies and grew them to large size. Since
the company went public very late, it was not very famous like listed companies
from India. Only after TCS got listed, the company started growing faster. In
the Initial years, TCS was competing on
low cost strategy and Infosys was competing on premium
pricing strategy. If TCS was listed in 1995, today, the company would have
become the Top software company in the world.
TCS
realised that the services provided by TCS were no less than competitors. It Started
pricing IT services appropriately which increased the margins as well as more
client wins.
In the
initial years of the industry, the revenue was mainly from sending software
coders abroad. The local operations were subsidized by revenue from
International operations. In India, the companies were in the learning curve.
The Education Institutes were not ready to provide trained people to work in
the software industry. The candidates were recruited from Leading Institutes. The
industry players took lot of initiatives to train manpower and ensured that the
right manpower was available. This has helped to bridge the Talent pool. Now, there
are lot of training institutes and further, Companies have tied up with Education
Institutes to train manpower and included the relevant subjects in the curriculum.
In
1991, we participated in an All India Young
Manager’s competition on strategies for accelerating the exports from India. We made
our presentation on IT Services Exports with the assumption that the industry would grow by 25%CAGR
for another 20 years and how this would help in accelerating the exports from
India. Despite assumptions were explained
well and in very clear terms, none of the judges were willing to believe that
the software industry would grow at a fast pace. They argued against our
assumptions and not confident of the Industry achieving the target.
Later,
We were very happy that our predictions came true. The concept of automatic
software development was just picking up at that time. Software engineering
concept was taking off. About 5 to 10% of software coding could be automated at
that time.
Now
with the advent of AI, software coding can be automated to a very
large extent. I heard one of the Heads of the software company saying that 90 percent
of software development could be automated going forward. But there are others
saying that new jobs would be created and not much to worry. According to Tech
Republic While AI and ML will certainly change the
way software is developed in the future, they are unlikely to replace human
developers entirely.
Software automation tools are software programs or
platforms designed to automate various tasks and processes in the software
development and IT operations lifecycle. These tools help organizations improve
efficiency, reduce errors, and accelerate the delivery of software products and
services. There are various types of software automation tools, each serving a
specific purpose. Here are some common categories of software automation tools:
Continuous Integration and Continuous Deployment (CI/CD)
Tools:
Jenkins
Travis CI
CircleCI
GitLab CI/CD
GitHub Actions
Configuration Management Tools:
Ansible
Puppet
Chef
SaltStack
Containerization and Orchestration Tools:
Docker
Kubernetes
OpenShift
Infrastructure as Code (IaC) Tools:
Terraform
AWS CloudFormation
Azure Resource Manager (ARM) Templates
Test Automation Tools:
Selenium
Appium (for mobile app testing)
JUnit (for Java)
pytest (for Python)
TestNG (for Java)
Monitoring and Logging Tools:
Prometheus
Grafana
ELK Stack (Elasticsearch, Logstash, Kibana)
Nagios
New Relic
Security Testing Tools:
OWASP ZAP (for web application security)
Nessus (for vulnerability scanning)
Burp Suite (for web application security testing)
Metasploit (for penetration testing)
Performance Testing Tools:
Apache JMeter
LoadRunner
Gatling
Locust
Version Control Systems:
Git (e.g., GitHub, GitLab, Bitbucket)
Subversion (SVN)
Build Automation Tools:
Apache Maven
Gradle
Ant
Workflow Automation Tools:
Apache Airflow
Zapier
Microsoft Power Automate
DevOps Collaboration and Communication Tools:
Slack
Microsoft Teams
Mattermost
Atlassian Confluence and Jira
Code Review and Quality Assurance Tools:
SonarQube
Crucible
ESLint (for JavaScript)
Pylint (for Python)
Release Management Tools:
GoCD
Spinnaker
Database Deployment and Versioning Tools:
Liquibase
Flyway
These tools can be used individually or integrated into
comprehensive DevOps toolchains to automate and streamline the software
development and deployment processes. The choice of tools depends on the
specific needs and technologies used by an organization or project.
One of the strategies to follow in accelerating the development of
Software is Low code and no code. Low code and no code are two approaches to
software development that aim to make it easier for people with varying levels
of technical expertise to create software applications.
Low Code: Low code platforms provide a visual interface
and pre-built components or templates to simplify the application development
process. These platforms are designed for developers but require less manual
coding than traditional development. Developers can use drag-and-drop
interfaces to create user interfaces, workflows, and logic, often supplemented
with some coding when necessary. Low code platforms are useful for accelerating
the development of applications while still providing flexibility for more
advanced coding when needed.
No Code: No code platforms take simplification a step
further by targeting users with little to no coding experience. These platforms
often offer a completely visual approach to building applications, relying on
pre-built components, templates, and a simple interface. Users can create
applications by connecting elements through intuitive interfaces, reducing the
need for traditional coding skills. No code platforms are typically used for
automating business processes, creating simple apps, and prototyping.
Both low code and no code platforms aim to democratize
software development, making it more accessible to a broader range of people,
including business analysts, designers, and subject matter experts. These
approaches can help organizations speed up application development, reduce the
backlog of IT requests, and empower non-developers to take a more active role
in creating digital solutions.
The impact of automation and AI on software development jobs is a topic
of ongoing debate and research, and there isn't a definitive percentage that
can accurately predict how many jobs will be replaced.
Automation and AI in Software Development:
Automation and AI have already started to transform various aspects of software
development. This includes automated testing, code generation, bug detection,
and even the creation of basic software components. AI algorithms can also
assist in code reviews, optimize algorithms, and enhance software security.
Enhancing Efficiency: Rather than entirely
replacing software development jobs, automation and AI are more likely to
enhance the efficiency of software development processes. Developers can use
these tools to automate repetitive tasks and focus on more creative and complex
aspects of their work.
New Roles and Opportunities: While some routine
programming tasks may become automated, there will be a growing demand for
professionals who can design, implement, and maintain AI and automation
systems. Roles such as AI developers, machine learning engineers, and data
scientists are already in high demand.
Sector Variation: The impact of automation and AI
on software development jobs may vary by sector. Some areas of software
development, like web development, may see more routine tasks automated, while
other areas, such as machine learning and AI itself, are likely to see
significant growth.
Software
is one of the sectors which creates lot of service and high paying jobs in the
Economy. In this context, it would be appropriate to consider how to reduce the
impact of AI on reduction of Software
Development Jobs.
Preventing the loss of software
development jobs due to AI involves a combination of strategies that focus on upskilling,
adapting to changing technology, and finding ways to work alongside AI systems.
Here are some steps that can help mitigate the impact:
Continuous Learning and Skill
Enhancement: Software developers should focus on continuous learning and skill
enhancement. Staying up-to-date with the latest programming languages, tools,
and frameworks can make you more valuable in the job market.
Specialization: Consider
specializing in areas that are less likely to be automated. For example, roles
that require creative problem-solving, architecture design, and complex
decision-making are harder for AI to replicate.
AI Collaboration: Learn how to
work with AI tools and platforms. Familiarize yourself with machine learning
and data science concepts, as they can complement your software development
skills.
Adapt to New Roles: Be open to
shifting roles within the software development field. AI can handle routine
tasks, so software developers may take on more strategic and creative
responsibilities.
Soft Skills: Develop soft skills
such as communication, teamwork, and leadership. These skills are often
difficult for AI to replicate and can make you more valuable in collaborative
settings.
Ethical AI Development: Become
knowledgeable about ethical AI development practices. Being able to ensure that
AI systems are built and used responsibly is a valuable skill.
Freelancing and Consulting:
Consider freelancing or consulting, as this can provide more job security in a
rapidly changing job market. Many companies seek specialized talent on a
project basis.
Entrepreneurship: Explore
entrepreneurial opportunities by creating your own software products or
services. This can give you more control over your career and income.
AI is a
tool that can enhance productivity and efficiency in software development, but
it is unlikely to completely replace the need for skilled human developers. By
adapting, upskilling, and focusing on areas where human expertise is essential,
you can reduce the risk of job loss in the software development field due to AI
advancements.
In conclusion,
We will witness rapid developments in IT sector in the years to come which will
change the complexion of the industry. The IT companies should be agile to adapt
themselves to the emerging trends and change the business models appropriately.
The employees in the industry should continuously reskill, upskill and rightskill
to remain relevant.
