Predictive analytics has been changing the world as we know it. From sports to everyday business, predictive analytics can help you perform at a lean operation. It can even predict future problems and solutions to solve your day-to-day tasks. What if I told you predictive analytics can also help you can develop software faster with fewer defects? How about technology that tells you what to expect before even starting development? With predictive analytics, the way we develop software is dramatically changing.

Predictive Analytics and Software Development

The combination of predictive analytics and software development can lead to a quicker delivery of higher quality software.


From testing to running the actual build, a large amount of data is produced when building software. Most of the time this data is viewed as temporary, but what is often missed is that data can be used for future projects.

Once a proper amount of data has been collected, the next step is creating the right  prescriptive analytic algorithms.  These algorithms can quickly read through the data to detect patterns that aren’t able to be seen from the human eye.

This detection helps lead to the automation of quality decisions and according to the 2016-17 World Quality Report , 40% of respondents will be looking to use these type of algorithms for automation testing.

The Right Pieces to Creating an Algorithm

There are two pieces to creating a predictive algorithm: the data and the algorithm. It is necessary to understand both when it comes to using them.

In order to create the proper algorithms, one needs to make sure they have Big Data. Big Data is defined as data sets that follow the “three V’s” of velocity, variety, and volume. Data sets that follow this have large amounts of data (volume) that vary across from test results to log files (variety) and that are able to process at a high speed (velocity).

Once you identify the necessary data, it is the data scientist’s job to ensure the predictive delivery through choosing the right algorithms. Each problem being solved is unique and algorithms have to be tailored to both the data being used as well as the issue at hand.

With the data and the algorithm, the development should be able to identify patterns and forecast future problems that a business or a user might face.

It helps a company gain a competitive advantage as it allows them to plan accurately for the future by recognizing patterns and behaviors that might not be obvious but are entirely relevant.

Future with Machine Learning

We are far from a computer being able to fully develop by itself, but with aspects of machine learning and predictive analytics, we are not only taking steps closer to this fantasy but also making our lives easier when it comes to developing software. More integration of predictive analytics into the software development life cycle will result in the further importance it plays when delivering software.

We are already seeing advances in this field whether it is Spotify suggesting music based on your listening preferences to digital ads popping up using your past search history. Eventually with the improvements in technology, one day computers will be able to predict on any of your preferences through just the data you produce, and may be even to predict your thoughts before they even happen!

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