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Machine Learning and Software Testing in 2019

by Jessica Cyrus, from Nexsoftsys, on Nov 29
Let's go through the relationship of Machine Learning and Software Testing and its impact to this modern era.

Analyzing the quality of your software is one of the most fundamental tasks in an enterprise. When you think of a product for your customers, you want it to be perfect. You imagine it to turn out to be something that quintessentially fits into your customer’s life. Because you know that there is nothing more important than the satisfaction of a customer, which helps you earn both revenue and reputation in the market.

As comforting as it may sound to design and deploy a dream product, it is equally difficult to implement it in practice. The recipe of perfect application software is far more complex than it seems which is why most organizations don’t get it right. A majority of enterprises even in today’s generation test their products once their complete software is built.

This not just puts the burden on the testing team but also escalates the delivery deadline to some extent. As a result of the pressure arising from the need to deploy a product within the timeframe, some defects are still left in the software. Having said this, organizations are left with a bad reputation and suffer losses in revenue.

Software testing is one of the key components of designing picture-perfect enterprise software. However, organizations need to pay attention to the timing when they start testing their software, as it can make or break their business. Testing your application at the right time can have a crucial impact on your organization and your product. For this reason, companies all across the world are not relying on manual testing methods. They are exploring fresh opportunities and technologies in the software testing domain that can help them perform better concerning the quality assurance of their products.

One of the popular adoptions that we’ve seen in the field of software testing is automation. Software Testing Services Organizations all across the world are utilizing automation as part of their testing strategy. This is helping them save a lot of time and costs. While manual testing is not completely dead, automated testing is picking up the pace and assisting companies to automate a lot of manual tests. Gone are those days when testing was one of the last parts of the software development life cycle.

With automated methods in today’s generation, software testing doesn’t need to be performed conventionally. Developers and testers do not wait until the entire application software is built. Instead, they are using automates testing tools to test a piece of code as soon as it is written. As a result, this is helping in finding bugs and defects at early stages of the application development.

Machine Learning for Testing Application Performance

Another technology that has gained the attention of testers in the last few years is machine learning. ML models are radically transforming every other industry including software testing industry for the good. It is helping organizations dive down into the nuances of their projects and come up with better insights. Not only this, but it wouldn’t be incorrect to say that ML is the gateway to the future, with its capabilities like predictive insights and more.

Even though machine learning and other ideas of artificial intelligence are in their infancy stage right now, there are a large number of problems with automated testing that can be addressed with the right algorithms. Some of these are:

  • ML can help detect any changes in the software and throw light on whether it is a bug or a new feature that should be tested.
  • It can generate unit tests based on the new features that are added in an application.
  • It can analyze the code quickly to analyze the test coverage.
  • One of the best use cases of machine learning can be predicting and timely notifying about any likely code or test issues.
  • It can speed up the maintenance and test runs by 10X
  • It can also help prioritize test cases and create dashboards to unite and share data on tested code.

Fundamentally machine learning models are trained to process the data, identify schemes and patterns and ultimately create and evaluate tests. The best part is that all this happens without the intervention of human efforts. Deep learning and models based on neural networks imitate the functioning of neurons in our brain and self-educate themselves based on the sets of data provided. Sean Bradley, Co-Founder of AudioEye points out that machine learning has come a long way from helping humans to evolving firms in the modern age.

Today, Machine learning can identify accessibility errors; associate those errors with specific elements and contexts along with fixing them on their own. Similarly, when errors of the same kind are spotted by the ML models, it can be easily determined whether or not they can be fixed. This can be done with the help of the results of heuristic analysis.

ML models are evolving day by day to reduce the dependency on manual testing, making it far less time consuming, more targeted and more effective. In other words, ML today is making computers smarter and reducing the burden of manual tasks from human shoulders. From suggesting potential fixes of accessibility to identifying defects that require human intervention, Ml is evolving software testing. With this, manual testers can strategies better and concentrate on more critical testing tasks.

ML has also found its use case in DevOps testing for organizations. One of the crucial areas where testers have implemented ML models in DevOps is application performance management. Put differently, machine learning is needed to identify volume, velocity along with the variety of big data that is generated by today’s dynamic application environments. When ML identifies anomalies and patterns in these situations, DevOps teams can troubleshoot better. They can analyze complex problems, understand usage patterns, reduce bug rates and improve customer experience.


It is imperative to understand that modern application software is based on a variety of elements. These include containers, microservices, cloud-based services, storage functions and other slices of computing. Moreover, the mesh of these complex combinations is changing rapidly. Issues in the performance that arise at the user’s end can have a root cause hidden somewhere among the thousands of objects from the mesh. With this, it becomes nearly impossible to identify whether the problem exists in an organization’s network, code, infrastructure or the end user’s device. Having said this, the best approach to solving this kind of a problem would be to work in a collaborative environment of big data and machine learning.

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