There is a constant increase in the complexity of software products due to a constant increase in the demand for quality products. On the other hand, companies of any size cannot release a software product without proper testing. To complete software testing on time, they need to have immensely experienced test engineers, effective tools, relevant infrastructure, and then the companies also need to decide what to test as a top priority. Predictive Analytics is a data driven technology which can help companies in making proactive decisions at the right time. Companies need to apply Predictive Analytics on software testing to predict failure points in testing activities at the earliest stage.
Many of the software testing companies follow industry best practices for any project in an effort to mitigate operational issues and costs but still many companies encounter many issues with every new project. The common problems are sudden increase in costs, production delays and operational risks. All these problems lead to difficulties in managing testing for multiple releases of various types of applications. Difficulties also arise in managing multiple testing tools, required infrastructure usage and productivity.
Let’s have a look at challenges and expectations:
- Different stakeholders have different expectations
- Unable to generate KPI (Key Performance Indicator) reports, Audit reports, Test management reports etc.
- Not able to produce desired and on-demand analytical reports
- Projects not progressing in the right direction
- Unable to identify right testers for right assignments
- Incurring huge testing costs?
- Meeting strict deadlines
- What measures should be taken to accomplish the project with in the set deadline when projects get deviated
- Unable to measure testing team productivity and identify suitable testers for a particular task
- Unable to identify issues which can lead to challenges in future
- Unable to provide required reports in preferred view
- Unable to measure test coverage and quality of work with timely alerts and notifications.
There are three major models which can be applied in Predictive Analytics and they are:
- Predictive model
- Descriptive model
- Decision model
Based on the KPI requirements or expectations of the stakeholders, a suitable model can be used and preferred reports can be generated.
The predictive analytical solution will help in addressing many questions from the existing testing tool and the questions are:
How will it affect my Testing project?
- How to improve the things?
- What is the right solution for a complex problem?
Predictive Analytics in an Integrated Approach
In a testing practice, multiple things are performed and different testing tools are leveraged to meet the requirements. When each testing tool works in siloes, then respective data and logs also get stored in siloes. Using emerging techniques and technologies and integrating predictive analytical tool into the integration framework can help companies in optimizing the cost, time and effort.
Advantages Predictive Analytics in Software Testing:
- Find right testers for right tasks
- Monitor overall project status
- Find issues impacting various areas of project
- Proactively identify risks and mitigate risks at the earliest
- Identify where is the delay and what is the issue
- Monitor testers and testing team productivity
- Make right decisions at right time
Conclusion
Predictive Analytics can empower a software testing department of company to mitigating risks in testing and deriving better ROI early in the testing life cycle. Predictive Analytics also ultimately helps in making Right Decisions at Right Time.
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