What are the best statistical techniques for test reporting and analysis in continuous integration?

1 Descriptive statistics

Descriptive statistics are the basic measures that summarize your test data, such as mean, median, mode, standard deviation, range, and frequency. They can help you understand the distribution, variability, and central tendency of your test outcomes. For example, you can use descriptive statistics to compare the average test execution time, the number of failed or passed tests, or the percentage of code coverage across different builds or branches.

2 Inferential statistics

Inferential statistics are the methods that allow you to draw conclusions or make predictions based on your test data, such as hypothesis testing, confidence intervals, regression, and correlation. They can help you evaluate the significance, relationship, and impact of your test variables. For example, you can use inferential statistics to test whether a code change has improved or worsened the performance, reliability, or security of your software, or to identify the factors that influence the test outcomes.

3 Visual statistics

Visual statistics are the techniques that help you display and communicate your test data using charts, graphs, tables, or dashboards. They can help you highlight the trends, patterns, and outliers in your test results and make them easier to interpret and share. For example, you can use visual statistics to plot the test execution time, failure rate, or bug density over time, or to create a heatmap of the code coverage or a histogram of the test duration.

4 Exploratory data analysis

Exploratory data analysis (EDA) is an approach that involves applying various statistical and graphical techniques to explore and discover insights from your test data, without relying on predefined hypotheses or models. It can help you uncover hidden features, anomalies, or relationships in your test results and generate new questions or hypotheses. For example, you can use EDA to examine the distribution, shape, or outliers of your test data, or to find clusters, groups, or associations among your test variables.

5 Machine learning

Machine learning (ML) is a branch of artificial intelligence that involves using algorithms and models to learn from and make predictions based on your test data, without explicit programming. It can help you automate and optimize your test reporting and analysis tasks and provide more accurate and actionable insights. For example, you can use ML to classify your test cases, prioritize your test suite, detect and diagnose test failures, or recommend test improvements.

6 Statistical process control

Statistical process control (SPC) is a quality management technique that involves using statistical tools and charts to monitor and control your test process and ensure its stability and consistency. It can help you identify and eliminate the sources of variation, errors, or defects in your test activities and improve your test efficiency and effectiveness. For example, you can use SPC to track and compare the test metrics, set the control limits and thresholds, or trigger the alerts or actions based on the test performance.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?