How Machine Learning is Bringing a Revolution in the Software Development Paradigm

Machine learning – can it be used for the development of traditional software development lifecycle? Since AI and other techniques have been increasingly becoming popular as key components in the development of modern software developments. Having said that, in such case, the fusion of AI & Machine Learning seems a more likely future. According to the University of Gothenburg paper, since Artificial Intelligence and Machine Learning are constantly being broken down and can be easily be used, even by the ones who aren’t experts in the field. The breakthroughs in software engineering have always helped AI capabilities to be reused via RESTful APIs as automated cloud solutions.

Artificial Intelligence Impact:

Artificial Intelligence is a world of unlimited possibilities in terms of the design, creation and testing of softwares. According to Forrester Research Survey, AI can also help with code generation. The survey also revealed that the AI software can write codes for any software and can implement it, even though it is its own idea and program the software itself accordingly. No, we’re not kidding. As a matter of fact, State of Testing Survey in 2017 revealed that testers would spend more time and resources on testing hybrid applications, once AI can take care of development and implementation process.

Artificial Intelligence Impact

Image Credits: Shawn Ennis

Real-Time Examples of AI Integration in Software Development Cycle:

Real-Time Examples of AI Integration in Software Development Cycle

Image Credits: Data Science Central

Google bugspot tool w3C: Since Google code base comprises of codes that is changed more than 50 percent every month. Therefore it leads to the increase in the team size it would become more unlikely that the submitter and reviewer would be aware of all little changes done on the hot spot. In such cases, the Bug Prediction tool uses ML algorithm and statistical analytics to find out if there are any errors. Source based metrics could be used for prediction and lines of code and the dependency that are cyclic or not.

Stack Overflow AutoComplete: Emil Schutte’s Code Complete is a case on point where the developer helped Stack Exchange data to crack out fully operational code based on the facts inferred from the existing codes.

Deep Code: The new AI code assistant that is developed by a Zurich based startup is a new AI programming tool that learns from a list of 2,50,000 rules from both public and private GitHub repositories, telling programmers to fix their code. In simple terms, it is done by a code review and helps in finding bugs in the code to help developers in delivering clean and reliable code.

Possibilities of AI To Play A Major Role:

Possibilities of AI To Play A Major Role
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Bug Fixing: Being one of the biggest areas to be revolutionized with AI technologies, because of the huge volume of data that needs to be carefully tested and in such cases the overlooking of bugs seems to be a natural human error. Software testing tools such as bugspots can represent programs that can help AI algorithms to auto-correct themselves without any human intervention.

Code Optimization: Fixing the old code, the compilers need the original source in a limited period of time. These compilers are themselves programs that process high-level programming language and convert it into machine language or instructions. The Helium software developed by Adobe and MIT Computer Science and Artificial Intelligence Laboratory automated the task of fixing old code, without requiring the original source. Making the code faster, this task would take an engineer upto three months or more and was reduced to mere days. The Helium software was used to optimise the performance of Photoshop filters by up to 75 percent.

Testing: AI-driven testing has been going around for some time and there are several open source tools that uses AI for generating test cases and performing regression testing. The AI-based software test automation tool, Appvance uses automation tool AI for performance and load testing and to generate test cases on user behaviour.

With infinite number of possibilities, the scope of machine learning is huge and still growing. In terms of software development paradigm, machine learning and artificial intelligence have and will play a crucial role in the near future.

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