BY — Marak Technologies
The once much sought after technology, Artificial Intelligence (AI) that data scientists had been trying to create, by transferring human-level knowledge and decision-making to computers has emerged like a Phoenix from the ashes. Assisted by Cloud Computing and Big Data, AI is one of the most revolutionary technologies that one can imagine, making its mark everywhere from cooking food to driving a car and other more complex areas.
Artificial Intelligence is transforming business functions in every job sector and the software development industry is no exception. Traditional methods of software development, assisted by AI and machine learning processes are much faster, smarter, and more effective than ever. This, in the process, is empowering programmers to discover patterns, repetitions, and trends in data sets in an automated way.
While AI does bring its share of advantages for the software development industry, there are certain arguments that surround the technology. To understand them, let us take an in-depth look into the ways that AI will bring a change in software development.
How AI will change the Software Development scenario
In the field of software development, Artificial Intelligence potentially supports the improvement of speed and accuracy of the processes. In fact, with the application of AI, the future of software development and its processes would be more simplified than ever. The technology will eliminate the need to solve inevitable problems by employing individuals to use their logic and write codes that need to adhere to rules; With the help of AI, all of this can be done with ease.
AI, today is more than capable of learning on its own like Google’s DeepMind algorithm that learned how to win 49 Atari games all by itself. Hence, it only leads to saying that software developers only need to feed appropriate data and train the neural network, after which the AI-powered tools take charge of doing the rest. Believe it or not, software development will bear witness to a colossal change in terms of programming and presentation to customers. Speaking of which, here are some major areas in software development wherein AI will make a difference.
AI for bug-fixing
A good example to show the use of AI, in this case, is SapFix, an AI hybrid tool created by the engineers at Facebook, which is designed to fix bugs in many ways, depending on their complexity. Additionally, the AI hybrid tool creates patches in case of smaller bugs that revert the code submission that introduced them, whereas, for more complicated bugs, the tool employs a bunch of “templated fixes” that were developed manually by humans in accordance with any previous bug fixes.
When it comes down to getting a makeover by AI for software development, bug fixing is an important aspect. The reason being that with time the volume of data that needs to be tested increases many times over, which leaves a gap for human errors and overlooked bugs. However, with the assistance of bug fixing tools that are backed by AI and Machine Learning algorithms human intervention can be eliminated and the codebase can be auto-corrected without any errors. In the process, the software development company also saves time and unnecessary revisions of the errors.
AI will reduce unnecessary workload for Developers
AI-based programming assistants are winning over the hearts and minds of many advanced developers because of the amount of time and tedious tasks that the tools offer. To elaborate on this point we can take the example of the new AI technology specifically built for programmers, Clever-Commit, which is a combined initiative of Ubisoft and Mozilla. The AI-based coding assistant uses techniques of machine learning and big data to learn from existing bug and regression data via which the assistant examines and flags potential new bugs as new code is committed. Additionally, the AI-based assistant is also designed to apply reinforced learning techniques to detect the cause of a bug and suggest fixes and aims to free up to 20% of programmers time, allowing them to concentrate more on other important tasks.
Quicker creations of MVPs
Minimum Viable Product (MVP) is an important component in the software development process. It is the medium via which a software development company can communicate its product/service to the target audience. In terms of software development, it is related to the creation of a website with sufficient features that can fulfil the demands of early adopters.
In the case of the traditional methods of programming, software developers would require to dedicate months of research and planning to begin a project and then they had to undergo the gruelling task of preparing a reliable sample so that it could be funded.
Fast-forward to the same method, but with the use of AI-powered tools. The process becomes simpler. Machine learning in AI shortens the time span from the beginning of the MVP cycle to the funding phase by only using a few lines of codes.
In the software development lifecycle, testing of a product crucial importance and is one of the most complex tasks that any developer comes across. One of these tasks mainly involves scheduling all the possible outcomes and cases that can be received or executed on a particular software to check its ability.
To elaborate on how AI can help with the testing process, we can take the example of Qualitest, the world’s largest Independent software testing & Quality Assurance Service company, who was approached by a client, who reported that some of the customers on their web-based application were facing data problems in generated ad hoc reports. The challenge of the project was that the client generated over 4 million reports per year and generating ad hoc reports from this data was a mammoth task.
Qualitest took to applying pattern matching, to analyze whether A.I. could help to recognize patterns in production behaviour and thus narrow their focus in the project. The initial plan that was put into action was the use of production driven test coverage and combinatorial analysis, based on online analysis of data and metadata as indicators of functional usage gaps between different environments.
Qualitest later took into consideration working with the front-end application as well as the back-end processes so that they could get access to existing data that could help them understand the client’s web-based application’s structures and various application dimensions. Qualitest expanded their analysis of data from weeks to months after which they carried out the process of making comparisons with test data. In the end, via the use of their AI tool, Weave, the endeavour for finding the patterns that they sought was successful. Qualitest was able to generate many bar charts to explain the concentration patterns and how they differed between the production and QA efforts.
Will AI replace Software Developers?
The change brought about by AI is getting so big that software developers around the world are concerned that it might take over their jobs. To support the argument, a study by Oxford University warns that software development will require to keep up with high-quality and error-free codes if they want to stay to beat match the ability of AI, which will also have the power detect any coding errors without much effort. This is especially true for industries that are safety-critical.
But, even then there are studies that contradict this and say that AI will improve code but not replace developers. In fact, according to a study by Gartner Inc., the IT research firm, AI is predicted to generate over two million new jobs by the year 2025. AI will improve the productivity of many jobs, eliminating millions of middle- and low-level positions, but also creating millions of new positions of highly skilled, management and even the entry-level and low-skilled variety.
Author @ Ibanrilin Diengdoh
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