Software Engineering Analytics

What are Software Engineering Analytics?

Software engineering analytics refers to the practice of collecting, analyzing, and interpreting data and metrics related to the software development process. This data-driven approach is used to gain insights into various aspects of software engineering, with the goal of improving productivity, quality, and decision-making. 

Software engineering analytics can encompass a wide range of metrics and data sources, including code quality metrics, development cycle times, defect rates, team performance, customer feedback, and more. These analytics provide a quantitative and objective view of the software development lifecycle, helping teams identify areas for improvement, make informed decisions, and optimize their processes. By leveraging software engineering analytics, organizations can enhance their software development practices, deliver higher-quality products, and meet project objectives more efficiently.

Data Analytics vs Software Engineering Analytics

Software engineering analytics and data analytics are related fields, but they focus on different domains and objectives:

Software Engineering Analytics:

• Domain: Software engineering analytics specifically focuses on data and metrics related to the software development process, including activities such as coding, testing, deployment, and maintenance.

• Objective: The primary goal of software engineering analytics is to improve the software development process. It aims to enhance productivity, quality, and efficiency in software development by analyzing metrics like code complexity, defect rates, cycle times, and team performance.

• Data Sources: Data for software engineering analytics is typically sourced from software development tools and processes, such as version control systems, issue tracking systems (like Jira), continuous integration/continuous deployment (CI/CD) pipelines, and code repositories.

Examples of software engineering analytics include measuring code churn, identifying bottlenecks in the development pipeline, and tracking the effectiveness of code reviews.

Data Analytics:

• Domain: Data analytics is a broader field that can be applied to various domains, including finance, marketing, healthcare, and more. It focuses on examining and extracting insights from data to inform decision-making in these diverse areas.

• Objective: The main objective of data analytics is to discover patterns, trends, and insights within data. It can be used for purposes such as predicting customer behavior, optimizing supply chains, or conducting market research.

• Data Sources: Data analytics can use data from a wide range of sources, including databases, web data, sensor data, social media, and more. The data sources depend on the specific domain and objectives of the analysis.

Examples of data analytics include predicting stock market trends based on historical price data, segmenting customers for targeted marketing campaigns, or analyzing patient data to improve healthcare outcomes.

In summary, while both software engineering analytics and data analytics involve analyzing data to gain insights, they differ in their domains of focus, objectives, data sources, and application areas. Software engineering analytics is specialized in improving software development processes, while data analytics is a more general field that can be applied to a wide range of industries and domains.

Software Engineering Metrics

Metrics in software engineering contribute to the development, maintenance, and overall success of a software project. Tracking the right metrics is the first step towards deriving useful analytics.

Try these 10 metrics to get started, or download the 10 KPIs Every Engineering Leader Should Track to learn more!

Metrics for Investment & Capacity: For many organizations, the engineering team is the largest investment your company is making, so it’s important to ensure that this investment is being pointed in the right direction. Two metrics to track here are:

• Allocation
• Hiring & Ramp Time

Metrics for Quality: helps ensure that the software being developed can provide that value to your customers consistently. For quality, track these metrics: 

• Bugs
• Time to Resolution
• Uptime

Metrics for Process: helps teams to set proper expectations, drive alignment across functional teams, and allow for better execution and better penetration of the market. For process metrics, track:

• Cycle Time and Lead Time
• Deployment Frequency
• Task Resolution Rate Over Time

Metrics for Deliverable Progress & Forecasting: measure and report on the progress your team is making toward that value creation in order to drive alignment across all the teams in your company. Two metrics to track are:

• Completion / Burndown Percentage
• Predicted Ship Date