Predictive Analytics in Modern Software Testing

Software testing involves several processes, including finding suitable testers for the project, fixing the budget, setting up a deadline, and estimating challenges.

On top of existing methods, the innovations and latest trends in this field pose additional challenges (like finding those skilled with these technologies and choosing testing methods (manual or automated)).

On top of existing methods, the innovations and latest trends in this field pose additional challenges (like finding those skilled with these technologies and choosing testing methods (manual or automated)).

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Predictive analytics is an extension of data analytics that utilizes machine learning, AI, and statistical models, among other techniques, to determine future outcomes.

It is an excellent tool for forecasting, fraud detection, and predicting customer behavior.

Critical Components of Predictive Analytics in Software Testing

Several components contribute to the success of predictive analytics in software testing.

Each mentioned component works together to analyze data and forecast information so the management can make more informed decisions.

1. Data Collection

Predictive analytics is only effective if it can deliver actionable insights and data collection, and its preparation remains the first step in this direction.

Testers can complete this step by utilizing version control systems, issue tracking systems, application monitoring tools, and user interaction and feedback tools. Kinds of data that is used in predictive analytics include-

• Historic Defect data
• Code metrics
• Test metrics
• Process metrics
• Environmental data

2. Data Storage

After collecting data, storing it in the right places is crucial. Relational databases like MySQL, NoSQL databases like MongoDB, and data lakes like Amazon S3 are some excellent options. However, QA engineers should consider the scalability, performance, and security of these available options before including them in the testing process.

3. Data Processing

QA professionals focus on this technical step, which involves removing duplicity, managing missing values, normalizing data, and encoding categorical variables from the data sets. Testers use Python (Pandas and NumPy libraries) and R to complete this step.

4. Feature Engineering

The process is all about creating and refining features (input variables) from raw data to improve the performance of predictive models.

You can apply practical steps like exploratory data analysis and iterative processes, utilize tools (like featuretools) and involve domain experts in identifying relevant features to help improve feature engineering. It involves techniques such as

• Extract Features
• Feature Transformation
• Feature Selection
• Creation and Interaction Features

5. Model Selection and Training

Testers should be careful when selecting predictive analytics models; these should be created to predict software defects and estimate or optimize testing efforts. There are two main steps included in this process, as listed below.

• Model selection- Choose algorithms according to the problem type and data characteristics. Regression, classification, or time series models are the choices you can opt for.
• Training and validation- The step includes splitting data, applying cross-validation so the data standardizes, and hyperparameter tuning using grid search or Bayesian optimization.

6. Model Evaluation

Predictive analysis is a continuous process, and proper maintenance of the models helps keep pace. The evaluation of these models includes evaluating how these models perform in finding out anomalies in the product/ service under test. Its two components are discussed below.

• Accuracy metrics -

1. It uses precision (classified as positive), recall, and F1-Score.
2. Precision = True Positives/ Sum of True and False Positives.
3. Recall= True Positives/ Sum of True Positives and False Negatives.
4. F1-Score takes into account the false positives and false negatives.

• Confusion matrix -

It is a table with 4 components- True Positives, True Negatives, False Positives, and False Negatives. It helps offer a clearer picture of the model’s performance, where it excels and struggles.

7. Prediction and Decision-Making

Predictive analytics in software testing uses historical data to forecast future outcomes. Analyzing past defects, testing processes, and application performance can help identify potential problems and improve decision-making.

• Risk prediction — ML algorithms, statistical models, and data mining techniques are used on the historical data to predict potential risks.
• Test optimization — The process results in streamlining testing efforts, effective resource allocation, and improving the test coverage. Some approaches to delivering these results include test optimization, test automation, and test data management.

Data Types QA Collect for Predictive Analytics Models

The primary step in collecting any data type remains to identify different sources, such as customer feedback and inspection reports. The data so collected can help predict future outcomes and identify potential threats.

• Defect Data

Collect data and use standard methods to classify the defects, like codes, types, and severity.

The QAs can identify the app’s high-risk areas by analyzing defect data, predict future defects, and improve resource allocation.

Such data can optimize the testing efforts and enhance the overall quality of software by aggressively addressing potential defects.

• Testing-Related Data

The essential data types and sources for collecting testing-related data include test case data, test execution data, requirement data, and build and release data.

Test management tools like JIRA and TestRail, defect tracking tools like MantisBT, and code repositories like Git, among others, can help collect this data effectively.

• Development Data

You need to collect development data from the different stages of the software development lifecycle. This kind of data can help eliminate code quality issues and identify areas for improvement.

Some of its types are code quality metrics (like line of code and code coverage), commit data, branch and merge data, build data, and development activity data (like developer ID, task assignments, and task completion time).

A QA can collect this data from sources like version control systems, code quality and analysis tools (like SonarQube), and defect

• Operational Data

Test case data like the test case execution logs, test coverage reports, defect data like the defect logs, defect trends, and build and deployment data like build/ failure rates and deployment logs make up the operational data in predictive analytics.

Some techniques like regression analytics (with Statsmodel tool or use cases like test execution time), clustering (use case- group similar test cases), and classification (TensorFlow) help in running these analytics.

Application Data

User interaction data like clickstream data and heatmaps, error and exception logs like application logs and crash reports, performance monitoring data like application performance monitoring data, and user feedback and reviews are a part of application data.

Some predictive analytics techniques used with this data include use cases like predicting user engagement metrics and application response time for regression analysis and use cases like forecasting user engagement metrics over time and trends in application errors for time series analysis.

Different Types of Predictive Analytics Models

There are several types of predictive analytics models, and each is selected based on the nature of the data, business goals, and complexity of the relationship between variables.

1. Classification Model

It is one of the simplest models that answers yes and no questions, like whether the business introduces a new technology. It uses algorithms like Decision trees, Random forests, Naive Bayes, and Support vector machines (SVM). The model identifies the relationship between input and output variables to categorize the data accordingly.

2. Clustering Model

Here, the data is classified depending on similarities in the attributes of the input data. The model groups together the data that shares similarities for customer segmentation and market research. It can be used to group similar test cases or defects to optimize the testing process using algorithms like K-means, hierarchical clustering, and density-based clustering.

3. Forecast Model

By leveraging the forecasting model, the organizations can improve their QA processes. It can be applied in defect prediction, test effort estimation, release planning, predicting conversion rate, and resource allocation. It uses time series analysis like autoregressive integrated moving average (ARIMA), ML models like Random Forests and SVM, and ensemble methods like bagging and boosting.

4. Outliers Model

An outlier is a data point that deviates from the rest of the dataset. The model finds anomalies by analyzing individual instances and/ or connected numbers or entities (like people and organizations).

While using this model, you can apply statistical methods like Z-Score, modified Z-score, and boxplot, as well as ML algorithms like isolation forest, local outlier factor (LOF), and clustering techniques.

It can improve data quality, enhance model performance, and identify anomalies.

5. Time Series Model

The model analyzes historical data points at successive intervals to predict the future. It can be used in software testing to forecast the test execution time, resource utilization, and defect rates.

Benefits of Predictive Analytics in Software Testing

Predictive analytics can change the way software testing works. It uses AI and ML algorithms, statistical models, and data mining techniques to forecast future performance, trends, and user behavior.

Predictive analytics offers several advantages in software testing, and a few of them are listed below.

1. Improved User Experience (UX)

Different models under this category use historical data to predict customer behaviors. It improves UX by customizing personal interactions and optimizing content. For instance, Netflix and Amazon use predictive analytics as collaborative filtering to suggest movies and products.

Other areas of its application include behavioral targeting, churn prediction, sentiment analysis, and dynamic content adaptation.

2. Cost Optimization

By identifying defects at the early stages, predictive analytics can significantly reduce costs in software testing. The mentioned steps can help in this direction while implementing predictive analytics.

• Build features like defect density, code complexity, and test case execution history to collect historical data efficiently.
• Choose the proper modeling techniques while focusing on testing techniques in high-risk areas.
• Use ML algorithms to prioritize test cases by their probability to detect critical defects.
• Implement steps like data integration and automated reporting to generate real-time insights.

3. Regression Test Suite Optimization

Regression test suites are a group of selected test cases that help find flaws in the code when a change is introduced. Predictive analytics can optimize this process by collecting the test data and identifying its defects.

It runs feature engineering tests with features like test case failure frequency, code churn, and execution time to optimize the testing process.

Interesting Read:- Regression Testing In Agile Scenario

4. Better Release Control

With predictive analytics in software testing, you can easily predict potential risks associated with the upcoming release. Testers discover their resource needs, and the analytics ensure optimal allocation.

Predictive analytics can also predict the best time to release the product/ service in the market, and all of these things help manage the release better.

Tools and technologies that help with predictive analytics in software testing?

Techniques of Predictive Analytics

1. Data processing and analysis
2. Machine learning libraries
3. Visualization
4. Integration with CI/CD
5. Defect tracking system

Corresponding Tools and Technologies

1. Python and R
2. SciKit Learn, TensorFlow, Keras
3. Tableau, Seaborn, Matplotlib
4. Jenkins, GitLab, Travis
5. Bugzilla, JIRA

Conclusion

Predictive analytics is a continuous process that is not limited to software testing. However, Its implementation in this process can significantly improve customer satisfaction with the product.

It promotes real-time learning (through feedback) and shifts the business’s focus on customer behavior/ usage patterns.

FAQs

Q. How is predictive analytics different from machine learning?

Although people may think the two technologies are similar, they aren’t. Predictive analytics and machine learning work hand-in-hand to uncover critical insights in large data volumes. Predictive analytics is broader and uses ML, data mining, predictive modeling, and statistics to anticipate prospective results.

Q. How is predictive analytics beneficial for any business?

Predictive analytics can help different businesses in different ways.

• Predictive analytics can generate intelligent insights into the future.
• The model can help businesses know which product/ service the users will accept.
• It can discover the features (app) that will generate better revenue.
• Financial entities can use this technology to mitigate risks.