February 23, 2017


Engineering Analytics – What’s next in software engineering?

Co - Authored By : Aman Bhardwaj  

Role of Data analytics in next generation software engineering

The conventional, tedious approach to software engineering is a thing of the past. Companies today are looking for smarter ways to develop and test their software.  Further, the rapid evolution of technology has led to a surge in the demand for effective and efficient product development models.

To gain the early-adopter advantage, time to market should be as low as possible – at the same time, the quality of the product cannot be compromised and has to consistently be at par with industry standards.

How do you deliver the best, in the least amount of time? Engineering analytics has the answer

What does Analytics contribute to the software engineering domain?

Software engineering analytics is the process of getting into the nuts and bolts of development & testing activities – uncovering quintessential insights and recommendations. It is a very complex and data-heavy activity, involving millions of lines of code, a huge bug database and complex testing frameworks. Dealing with such huge volumes and variety of data is indeed a mind boggling task - this is what makes software projects risky and unpredictable.

Research shows, 35% of incomplete projects are not abandoned until the implementation stage of the project’s life cycle. This can be attributed to poor project planning – managers are unable to utilize resources in a prudent way and monitoring/predicting risky avenues becomes difficult, even before project initiation. Because of this dissonance, the project management team is not able to pre-empt adverse consequences and hence, the project gets abandoned.

This chasm can be mitigated if they perform risk base analysis before project commencement. Engineering analytics facilitates past performance analysis, and identifies critical areas for future development and testing.

How does Analytics enable efficient and effective engineering?

 The software engineering landscape has witnessed a paradigm shift – from waterfall to agile to dev-ops methodology – eventually shortening the software development life cycle. This shift is also a result of the market’s gradual adaption to the Release Early Release Often (RERO) philosophy.  This continuous process of development and testing can only be facilitated by analytics.

50% of annual company revenue, across a range of industries, is derived from products launched within the past three years. This clearly indicates that companies no longer monetize older, ‘cash-cow’ products and must now look out for new products that better fit customer demands – data analytics plays a crucial role in enabling this.

Various analytical techniques – such as neural network, decision trees, clustering, and regression structures – are geared for resolving the pain points in software engineering. With the help of analytics, developers, testers and managers can gain insights to aid decision making. It may be related to root cause analysis, risk based testing, or efforts in triaging. Analytics is a one-stop solution to most real-time problems.

When it comes to the service industry, people are the most important resource – therefore, to beat the best in the market, optimizing of the skills of these resources is of utmost importance. Analytics helps us capture performance snapshots, identifying unique resource skillsets. This categorization helps in easy allocation of work according to resource capabilities. This ultimately results in improving job satisfaction, as well as boosting project productivity – a win-win situation for all.

Analytics:  Indispensable for engineering success

When William Edward Deming once quoted “In GOD we trust; all others must bring data”, he prophesied the importance of data and the ubiquity of data analytics across domains. Data analytics is the certainly a reliable way to taking smarter decisions. Decisions taken on the basis of data analytics are well grounded since they are backed by a series of logic and algorithms. Hence, analytics adoption must be a top priority for organizations that want to pursue transformation, inside-out, and outside-in.