There has been a gradual shift in focus with regards to software development. While it still is requirement based, more weightage is now being put on making it customer-centric. This change in approach has given rise to the need for predictive analytics solutions. Predictive analytics educates developers about customer opinions thereby making digital transformation an easier and easily achievable process, provided the other parameters are correct.
Today predictive analytics is a widely applied concept. Almost all industries and businesses derive the references required to make informed and effective business decisions from it. With traditional QA taking on more responsibilities, and quality and speed becoming imperative deliverables, taking an analytics-based approach:
- Enables developers to predict future failure possibilities,
- Helps extract critical project or business based information,
- Helps to generate patterns to make an estimation of future trends easy and
- Enables QA to take proactive decisions.
Thus implementation of predictive analytics solutions helps developers to analyze historical data and predict resource usage and user behaviour, moderate testing behaviour etc.
The need for quality within a short span of time tends to put greater pressure on software developers. They are tasked with improving software accuracy and quality within a short time. Thus they have to foresee and correct beforehand the challenges and failures that are likely to crop up when using the software. The biggest advantage of using predictive analytics is that it can predict future failures based on the analysis of historical data sources. This benefits software developers and testers who can create test scenarios based on the predictions made by the application of predictive analytics solutions.
What is predictive analytics?
Simply speaking it refers to making predictions of unknown future events based on data analytics. This is achieved by making use of 6 important techniques which are:
- Data mining,
- Artificial intelligence,
- Statistical algorithms,
- Machine learning and
The implementation of predictive analytics solutions is considered to be quite a reliable method of:
- Forecasting future developments, trends, faults and failures,
- Responding to upcoming challenges proactively and
- Capitalizing on future trends.
Key benefits of implementing predictive analytics to integrated software testing
Integrated software testing involves combining individual software testing units so that they can be tested as one single group. This helps expose faults or errors that can potentially occur when these individual units interact with each other. Thus integrated software testing helps determine the accuracy of the interacting interface between individual testing units and is hence done only after these units have been tested individually.
In integrated software testing multiple testing activities are performed using multiple tools. Each testing tool works and stores testing logs and data in silos. Implementing predictive analytics within this integration framework helps optimize cost, effort and money.
This approach can be deployed for both in-line and off-line production test optimization. The practice of extracting information available in data sets enables predictive analytics to forecast the likelihood of failures and errors in software used by customers. Combining historical data analytics with customer feedback gives its predictions the desired reliability. It also helps software development teams to perform better risk assessment thereby eradicating situations which can give rise to errors or failures. Thus by exploiting large datasets of relevant test data and logs more smartly, predictive analytics can easily transform current testing procedures making software applications customer-centric and qualitatively superlative.
Traditional testing techniques, in vogue so far, put up several challenges for developers which hampered the quality of the software developed by:
- Creating gaps between the final product and client expectations as usage patterns were ignored and more focus was placed on the technicalities involved, the commercial aspect etc.,
- Failing to adapt to real-time learnings due to absence of feedback loop.
- Being inward-focused and thus lacking in major details and
- Focusing more on functional testing and disregarding customer satisfaction.
Predictive analytics, on the other hand, helped solve almost all of these challenges by:
Understanding user behavior patterns: To ensure superlative customer experience, their behavioural patterns need to be understood and analyzed. This encompasses several factors like user reaction to a previously followed specific pattern, their response to a particular event or a series of events etc. This information is further evaluated to understand:
- Potential areas vulnerable to bugs,
- Events that can lead up to the formation of this bug and
- Reasons which can give rise to the same.
Thus an understanding of the user’s behavioural pattern enables the testers to modify their testing methodologies so that the possibility of the occurrence of the bug can be reduced. Furthermore, when testers have to perform cross-browser website testing, predictive analytics can help them choose the most appropriate browsers to ensure there is no lacuna in testing the website in commonly used browsers.
Making QA more customer-centric: For the applications to be customer-centric, consumer sentiment and the current overall market scenario needs to be understood. The application of predictive analytics helps traditional QA become customer-centric by focusing on certain key areas like compatibility issues, functional issues, security issues, performance issues etc. Thus it becomes easy to deliver contemporary solutions thereby enabling a superlative user experience.
Enabling prioritized testing activities: When software is developed and subsequently processed, massive amounts of information are generated. These comprise mostly defect logs, production incidents, test cases and results, project documentation, application log files and other such things concerning QA. Storage of this information is required so that it can be properly analysed for further improvisations. Predictive analytics solutions, when applied to this data enables software testing to become optimized and achieve better test coverage by:
- Evaluating its impact on user experience,
- Aligning test scenarios and
- Identifying issue patterns etc.
Defect data root cause analysis can also be enabled to predict application break down points with the identification of hotspots or weak spots within the application. Further optimization of the application development workflow process can speed up the development process.
Boosting of testing efficiency: Generally, QA teams use relevant tools, constantly keep monitoring the application log files and keep generating test scripts to ensure early detection of potential application defects and failures. This is called the shift-left approach in software testing and it ensures early error detection thereby reducing the occurrence of potential defects in the future. This approach gets a massive boost with the use of predictive analytics. For robust application delivery, this boost in testing techniques ensures the delivery of a compatible and secure application to the customer or client. Thus both the objectives of digital transformation and better customer experience are satisfied.
Resource management: Developing software applications requires a team effort for its successful planning, resource gathering, developing, testing and deployment. Each of these stages takes a certain amount of time which can be predicted by implementing predictive analytics solutions on historical data. The resource can accordingly be assigned and the software can be developed with greater efficiency.
Reduces time to market: Data processing in predictive analytics requires multiple algorithms like machine learning, regression algorithms and time series analysis. The complexities of QA and software testing emerge from the involvement of several dependent variables; all of which need to be efficiently managed to ensure compliance with the result expectations. The use of predictive analytics makes this rather easy by streamlining software testing activities and improving their performance.
Thus predictive analytics is that catalyst which when applied enables better QA and business decision making based on proven data points. Predictive analytics is a repetitive process as data generation occurs constantly during software development and thus needs to be continuously analyzed. This analysis adds value to the final output.
Today digital transformation is causing business dynamics to change. QA is playing a central role in this. There is no scope for error and every effort is being made to bring down the costs and efforts involved in software testing. Predictive analytics is the key which will ultimately make businesses deliver quality products faster.
How does predictive analytics help software testing?
The demand for reliability and speed in software testing has increased manifold. The bar has been set very high and today consumers demand only the best. Thus even minor details cannot be left out of testing. This is where the implementation of predictive analytics solutions can help developers and testers and ultimately the software development itself. There are a lot of advantages and benefits involved in using predictive analytics. They are:
- Software testing becomes more customer-centric,
- Better analysis of data results in the accumulation of relevant data and information is wealth,
- Enables sentimental analysis wherein customer sentiments collected by way of customer feedback is analyzed and the software developed accordingly,
- Better detection of defects,
- Test efficiency increases,
- Helps analyze what is working and what is not to ensure better application efficiency,
- Testing activity intuitions get simplified,
- Time and money efficiency etc.
Predictive analytics use three major techniques or models to achieve the above. They are:
- The predictive model,
- The descriptive model and
- The decision model.
The model to be applied or the predictive analytics approach to be taken depends on the expectations of the clients or his KPI requirements.
Predictive analytics is the future of integrated software testing. It overcomes challenges known till date and also has the potential to achieve better results in the future. What is known today is just the tip of the iceberg. There are many more potential uses and benefits yet to be discovered.