5 ways artificial intelligence is upgrading software engineering

47% of digitally mature organizations, or those that have advanced digital practices, said they have a defined AI strategy (Source: Adobe). It is estimated that  AI-enabled tools alone will generate $2.9 trillion in business value by 2021.  80% of enterprises are smartly investing in AI. The stats speak for themselves. AI clearly follows the motto “go big or go home”.

This explosive growth of AI in different sectors of technology is also beginning to show its colors in software development. Shawn Drost, co-founder and lead instructor of coding boot camp ‘Hack Reactor’ says that AI still has a long way to go and is only impacting the workflow of a small portion of software engineers on a minority of projects right now.

AI promises to change how organizations will conduct business and to make applications smarter. It is only logical then that software development, i.e., the way we build apps, will be impacted by AI as well. Forrester Research recently surveyed 25 application development and delivery (AD&D) teams, and respondents said AI will improve planning, development and especially testing. We can expect better software created under traditional environments.

5 areas of Software Engineering AI will transform

The 5 major spheres of software development-  Software design, Software testing, GUI testing, strategic decision making, and automated code generation- are all areas where AI can help. A majority of interest in applying AI to software development is already seen in automated testing and bug detection tools. Next in line are the software design precepts, decision-making strategies, and finally automating software deployment pipelines.

Let's take an in-depth look into the areas of high and medium interest of software engineering impacted by AI according to the Forrester Research report.

 



 

Source: Forbes.com

#1 Software design

In software engineering, planning a project and designing it from scratch need designers to apply their specialized learning and experience to come up with alternative solutions before settling on a definite solution.

A designer begins with a vision of the solution, and after that retracts and forwards investigating plan changes until they reach the desired solution. Settling on the correct plan choices for each stage is a tedious and mistake-prone action for designers.

Along this line, a few AI developments have demonstrated the advantages of enhancing traditional methods with intelligent specialists. The catch here is that the operator behaves like an individual partner to the client. This associate should have the capacity to offer opportune direction on the most proficient method to do design projects.

For instance, take the example of AIDA- The Artificial Intelligence Design Assistant, deployed by Bookmark (a website building platform). Using AI, AIDA understands a users needs and desires and uses this knowledge to create an appropriate website for the user. It makes selections from millions of combinations to create a website style, focus, image and more that are customized for the user. In about 2 minutes, AIDA designs the first version of the website, and from that point it becomes a drag and drop operation.

You can get a detailed overview of this tool on designshack.

#2 Software testing

Applications interact with each other through countless  APIs. They leverage legacy systems and grow in complexity everyday. Increase in complexity also leads to its fair share of challenges that can be overcome by machine-based intelligence. AI tools can be used to create test information, explore information authenticity, advancement and examination of the scope and also for test management.

Artificial intelligence, trained right, can ensure the testing performed is error free. Testers freed from repetitive manual tests thus have more time to create new automated software tests with sophisticated features. Also, if software tests are repeated every time source code is modified, repeating those tests can be not only time-consuming but extremely costly. AI comes to the rescue once again by automating the testing for you!

With AI automated testing, one can increase the overall scope of tests leading to an overall improvement of software quality.

Take, for instance, the Functionize tool. It enables users to test fast and release faster with AI enabled cloud testing. The users just have to type a test plan in English and it will be automatically get converted into a functional test case. The tool allows one to elastically scale functional, load, and performance tests across every browser and device in the cloud. It also includes Self-healing tests that update autonomously in real-time.
SapFix is another AI Hybrid tool deployed by Facebook which can automatically generate fixes for specific bugs identified by 'Sapienz'. It then proposes these fixes to engineers for approval and deployment to production.



 

#3 GUI testing

Graphical User Interfaces (GUI) have become important in interacting with today's software. They are increasingly being used in critical systems and testing them is necessary to avert failures. With very few tools and techniques available to aid in the testing process, testing GUIs is difficult.

Currently used GUI testing methods are ad hoc. They require the test designer to perform humongous tasks like manually developing test cases, identifying the conditions to check during test execution, determining when to check these conditions, and finally evaluate whether the GUI software is adequately tested. Phew! Now that is a lot of work.

Also, not forgetting that if the GUI is modified after being tested, the test designer must change the test suite and perform re-testing. As a result, GUI testing today is resource intensive and it is difficult to determine if the testing is adequate.

Applitools is a GUI tester tool empowered by AI. The Applitools Eyes SDK automatically tests whether visual code is functioning properly or not. Applitools enables users to test their visual code just as thoroughly as their functional UI code to ensure that the visual look of the application is as you expect it to be. Users can test how their application looks in multiple screen layouts to ensure that they all fit the design.
It allows users to keep track of both the web page behaviour, as well as the look of the webpage. Users can test everything they develop from the functional behavior of their application to its visual look.

#4 Using Artificial Intelligence in Strategic Decision-Making

Normally, developers have to go through a long process to decide what features to include in a product. However, machine learning AI solution trained on business factors and past development projects can analyze the performance of existing applications and help both teams of engineers and business stakeholders like project managers to find solutions to maximize impact and cut risk.

Normally, the transformation of business requirements into technology specifications requires a significant timeline for planning. Machine learning can help software development companies to speed up the process, deliver the product in lesser time, and increase revenue within a short span.

AI canvas is a well known tool for Strategic Decision making.The canvas helps identify the key questions and feasibility challenges associated with building and deploying machine learning models in the enterprise.

The AI Canvas is a simple tool that helps enterprises organize what they need to know into seven categories, namely- Prediction, Judgement, Action, Outcome, Input, Training and feedback. Clarifying these seven factors for each critical decision throughout the organization will help in identifying opportunities for AIs to either reduce costs or enhance performance.

 

#5 Automatic Code generation/Intelligent Programming Assistants

Coding a huge project from scratch is often labour intensive and time consuming. An Intelligent AI programming assistant will reduce the workload by a great extent.

To combat the issues of time and money constraints, researchers have tried to build systems that can write code before, but the problem is that these methods aren’t that good with ambiguity. Hence, a lot of details are needed about what the target program aims at doing, and writing down these details can be as much work as just writing the code. With AI, the story can be flipped.

”‘Bayou’- an A.I. based application is an Intelligent programming assistant. It began as an initiative aimed at extracting knowledge from online source code repositories like GitHub. Users can try it out at askbayou.com.
Bayou follows a method called neural sketch learning. It trains an artificial neural network to recognize high-level patterns in hundreds of thousands of Java programs. It does this by creating a “sketch” for each program it reads and then associates this sketch with the “intent” that lies behind the program. This DARPA initiative aims at making programming easier and less error prone.

Sounds intriguing? Now that you know how this tool works, why not try it for yourself on i-programmer.info.

Summing it all up

Software engineering has seen massive transformation over the past few years. AI and software intelligence tools aim to make software development easier and more reliable. According to a Forrester Research report on AI's impact on software development, automated testing and bug detection tools use AI the most to improve software development.

It will be interesting to see the future developments in software engineering empowered with AI. I’m expecting faster, more efficient, more effective, and less costly software development cycles while engineers and other development personnel focus on bettering their skills to make advanced use of AI in their processes.

Implementing Software Engineering Best Practices and Techniques with Apache Maven
Intelligent Edge Analytics: 7 ways machine learning is driving edge computing adoption in 2018
15 millions jobs in Britain at stake with AI robots set to replace humans at workforce