Machine learning will significantly impact software development in the following ways: 1) ML can analyze large amounts of machine data from software logs and metrics to improve security, performance, and reliability in an automated way that exceeds human capabilities. 2) ML techniques can be applied to code itself to automatically detect risky code, localize bugs, and provide intelligent IDE assistance across the entire development lifecycle. 3) ML has tremendous potential to continuously automate tasks like bug fixing, testing, deployment, and code optimization to deliver more and higher quality software faster with less human involvement.

1. 6/19/2020 How Machine Learning Will Affect Software Development?
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How Machine Learning Will A ect Software
Development?
venkat vajradhar
Nov 8, 2019 · 4 min read
High-grade software
Modern software systems release “machine data” (logs, metrics, etc.) that are critical
to detecting and understanding the abuse, but the size and complexity of this data far
exceeds the human ability to perform the necessary analysis and capture. Timely
action. For this reason, we see a lot of possibilities to create automated systems that
analyze (and work on) these machine learning data to improve the security,
performance, and reliability of financially complex software services.
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There is also a lot of exciting research around “ML on code”: automated detection of
risky pull requests, automatic bug localization, intelligent IDE help, and so on. Given
the well-known challenges of building and operating software systems, there is much
room for improvement over the entire lifecycle. Overall, I think we are going through a
really interesting time to apply ML techniques to software development, security, and
operations.
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ML has tremendous opportunities in continuous automation of bug fixing, testing,
deployment, and code optimization.
There is no possibility of replacing the human factor in software development. ML
could not decide which was right or wrong. It will continue to identify more tests that
can be automated. By solving and automating small tasks daily to make intelligent
decisions, you will be able to deliver more, faster, more quality, and with less human
involvement.
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NON-HUMAN LANGUAGE
The great promise lies in the pace of development and production — enabling us to do
so much more with our time. The simplicity with which we introduce feedback and
iterative cycles has allowed us to redirect and focus on outcome-led programming.
Capacity beyond what humans can achieve in software development.
There are also many possibilities for making the process of creating and producing
software very fast. But for me, the opportunity lies in the possibility of humans and
machines working together — moving the role of the programmer, developing new
skills and freeing them to focus on what they are good at, and allowing the machines to
be mundane.
Other
There are a lot of areas in tech that are going to see huge improvements from ML in the
coming years, but I am very excited about Discoverability — the process of finding a
product or experience. From grocery shopping to finding flights, searching for
information on Google, we all spend many hours a week doing these things.
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Teaching our priorities to the computer can help us do these things faster. We get more
free time, and in contrast to sectors like self-driving cars, no one will lose a job. It is a
pure universal good, and a machine learning helps.
Next-generation applications that use ML are seamlessly integrated and are in the
fabric of the ML app, so ML is working on real-time data, re-training, and testing, and
decision-making. Develop an integrated platform that integrates ML into the data
platform to power the required data size.
Allowing humans to focus on things like creativity and non-linear thinking requires
problems. Automate repetitive tasks. AI services are what builds intelligence. Use AI to
automate specific tasks around cleaning and preparing data and dashboard creation.
How to deal with technology without BI training.
More data scientists. With more open source ML libraries it is more accessible to
software developers.
There seems to be a great deal of potential for combining classical robotic algorithms
with ML. ML improves the performance of some of the algorithms while maintaining
the transparency of the original method.
DevOps can be a big winner. This becomes part of the software development process as
people begin to find value in the Ops data. Developers use tools if they are part of the
toolchain everyone uses. If ML-based data is part of the feedback loop of the dev
pipeline and code processes they are likely to use it. Subtle improvement in quality that
touches the intended features. They can see change for the better or worse over time.
ML SaaS-if is even more so. Some common problems come with the best models and
people are licensed and tailored to their use case. We stop building ML models from
scratch. We bring the best race to this is for application.
I believe that outside of most profitability, there is a vertical-centric approach. It is
difficult to build a one-size-fits-all horizontal solution, and successful companies can
spend their time in a column and make money from a specific problem.
Machine Learning Arti cial Intelligence AI Software Development

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