The purpose of software testing is to determine the risks to which the software product is exposed after it has been placed on the market.

While there are many ways to predict the performance of a solution, none is as effective and promising as predictive analysis.

In this article, you will learn how to use innovative technologies for software testing and how predictive analysis can help QA professionals optimize the effectiveness of job and performance evaluation.

What is predictive analysis?

By definition, predictive analysis is a subset of data science that uses old data sources to predict unknown events. The forecasting process is based on many technologies – intelligent data analysis, machine learning, statistical modeling, artificial intelligence and many others.

Forecast analysis process

Foresight analysis helps companies make data-driven decisions, actively manage products and processes, and develop effective strategies for the future.

How predictive analysis is used inSoftware testing

The success of software testing depends on the ability of the quality control and software development team to predict product usage behavior and ensure easy access to application visitors.

Unfortunately, due to a lack of awareness of the importance of user behavior or data for decision making, test teams tend to focus on meeting business and functional requirements rather than ensuring that the tool meets end user expectations.

As a result, quality professionals can overlook errors or inconveniences and free up a cumbersome and inefficient application.

By introducing predictive analysis into software testing, technicians have a clear understanding of how the user will react to the solution, what frustrations they may experience and how they will react to interface, performance or function failures.

Forecasting analysis has many applications, for example in the field of software testing:

  • As a tool to understand or predict user behaviour
  • Optimisation of resource management
  • Streamlining testing activities
  • Prediction of possible error locations

Data types used for predictive analysis models

QA Analytics - Data Scroll Wheel

Technical teams can use predictive analysis to build the data wheel, a set of data types that provide a clear understanding of how well the product meets functional criteria, business objectives and customer expectations.

These are the types of data that quality control teams rely on when introducing predictive analysis into software testing:

  • Defect data – measurements such as defect density, defect distribution, etc.
  • Test data – information for the analysis of test cases – test coverage, speed of testing, coverage of requirements, pass and fail rates, etc.
  • Development data – help determine the full coverage of the chosen testing strategy.
  • Application data – all knowledge related to the production environment.
  • Operational data – accident data, application monitoring statistics, etc.
  • Customer usage data – information on application behaviour, customer feedback and satisfaction, travel statistics of users.
  • Trade indicators help determine the extent to which a product meets trade objectives.
  • The data about the requirements are information about the relevance of the requirements and help to keep track of the changes.

Benefits of implementing predictive analysis inQC

As the number of platforms and systems that technology teams need to consider when releasing a product increases, a full assessment of software functionality and performance becomes more expensive and time consuming.

To achieve maximum test speed and meet user expectations, QA teams are starting to integrate innovative technologies into the planning, design and execution of test tasks.

In particular, predictive analysis has a wide range of applications in the field of software testing and consulting services, as well as a number of undeniable advantages:

1. Helps in making informed decisions

Forecast analysis enables business owners to collect data about potential software users and convert this data into forecasts of expectations, application behavior and potential disappointments.

Using predictive analysis tools and methods, quality assurance experts can develop test cases that correspond to actual user behavior and create a realistic production environment.

By approving the implementation of the final product, project participants can therefore be sure that no path or user path remains untried.

2. Better test efficiency

Forecasting analysis can help test teams estimate the time needed to implement different strategies.

By implementing the technology at the design stage, quality control specialists can ensure that the chosen approach is the most productive, cost-effective and timely.

3. Gaining competitive advantages over competitors

Insight into the impact of predictive analysis when testing software helps teams ensure greater operational efficiency, customer satisfaction and delivery of updates than competitors who do not rely on data-based forecasting and making assumptions-based decisions.

In addition to improving testing efficiency, collecting and processing more customer data can provide business managers with dozens of useful customer relationship information to share with marketing and sales departments.

The lack of a predictive analysis also puts the project group at a disadvantage compared to technically experienced competitors.

4. Risk management

Predictive analyses are invaluable in identifying and combating security threats. For example, the quality control team can design a predictive model that warns project participants when a red flag appears for a data attack or violation.

In addition to accurately identifying potential risks, a predictive analysis can help determine the severity of potential safety problems so that teams can prioritise tasks wisely.

By using predictive models, QA testers can be sure that they are not spending too much time solving a Level 3 problem while the main threat remains unsolved.

Forecast analysts improve implementation of annex

When it comes to integrating predictive analysis into the Software Development Life Cycle (SDLC), technical teams hope to reduce the cost of designing and testing software and meet user expectations with maximum accuracy.

A subset of the technology is most effective in three ways:

  • Forecast planning
  • predictive quality control
  • Predictive DevOps.

Let’s see how each of these frameworks improves application performance and reduces development costs.

1. Forecast planning

The inability to estimate the time, money and talent needed to complete the project is one of the most common problems in the implementation of the project.

For example, a predictive analyst can improve the process of strategy development and the quality of software consulting services:

  • Determine the time a developer needs to perform software engineering tasks based on the progress of previous projects.
  • Prioritize all functions of the application according to their importance to the end user, based on the interaction with the prototype.
  • Understand which characteristics are crucial for the company.

2. Predictable devices

DevOps’ practice is aimed at speeding up the application process. However, the efficiency of the system may double after the introduction of predictive analysis.

For example, technical teams can use predictive models to determine which coding methods increase or decrease customer satisfaction.

By collecting data on application failures and malfunctions, development and testing teams or the research center can understand which user actions are causing system failures and focus on these areas when designing test scenarios.

3. Predictive quality control

During the testing process, quality control teams often try to determine which sequence of code executed by users has led to a data leak, a system crash, an error or another malfunction.

By means of predictive analysis, testers will be able to locate the user paths that cause application errors.

In addition, predictive analysis can help find solutions to the most common problems based on the data collected throughout the test cycle.

Once the cause and origin of the bug, the similarities between the different types of bugs in an application are known, QA and the development team will know the scope of each bug, the other areas of the application that are affected, and the potential damage that the threat may cause.

Conclusion

Predictive analytics is a very useful set of technologies because it improves the quality of decision making, helps predict the impact of any change made by the software testing team and increases the efficiency of quality controls.

Technical teams should consider introducing predictive analysis methods at the earliest possible stage. The use of predictive models for planning and strategy development results in accurate estimates and efficient work processes.

Once the product is launched on the market, predictive analysis can be used to prepare for traffic peaks, detect fluctuations in customer satisfaction and identify risk areas that need to be updated.

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