Software Analytics

Software analytics is experiencing explosive growth thanks to the internet and proliferation of open source projects. Today there is so much data about software projects that it is almost impossible to manually browse through it all. Platforms such as Github host over 11.2 million projects.


What is Software Analytics?

In its narrowest form, software analytics is analytics on software data for managers and software engineers with the aim of empowering software development teams to gain and share insight from their data to make better decisions.

An important part of software analytics is that they include actionable advice about how to improve software projects. Due to the volume of data, finding these insights typically requires some degree of automation.

Examples of software analytics applications include:

  • Combining software product information with apps store data
  • Using process data to predict overall project effort
  • Using software process models to learn effective project changes
  • Using operating system logs that predict software power consumption


How is Software Analytics similar to IoT Analytics and what can we learn from it?

Software and IoT Analytics share the same challenges when it comes to the sheer volume and complexity of data. While software analytics focuses on bugs, processes and code anomalies, IoT Analytics focuses on data from things, processes and data anomalies. Both lead to an overwhelming level of complexity as well as time sensitivity that require some form of automation and analytics to provide timely insights that result in a positive business impact.

Both types of analytics are time sensitive and require data in real time so that the insights are still relevant and can provide value.

Decisions should be based on recent, not outdated data. Traditional data collection and analysis techniques might be too slow. In software analytics the current pace of manual methods in empirical software engineering often does not keep up with the fast pace of modern agile software practices. Similarly, the value of the data from things in an IoT scenario will also expire rapidly, causing a need for real time analytics in order to make the analytics actionable.

In both types of analytics, the analytics must provide timely and actionable advice. For example, if a manager is driving towards a cliff, they don’t need to be distracted by analytics reporting about the clouds in the sky or the flowers on the side of the road. Instead, actionable analytics need to shout, “There’s a cliff up ahead! Turn left immediately.”


The need for a platform for IoT Analytics

Vitria’s VIA Internet of Things (IoT) Analytics Platform as built to address the need for timely analytics, insight, and action in challenging IoT use cases.

VIA empowers organizations to improve their operational performance and drive revenue growth through business transformation with:

  • Faster, predictive analytics
  • Smarter decisions and actions
  • Better business outcomes


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