What are the most important software analytics methods for a software engineering manager?

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As a software engineering manager, you need to measure and improve the quality, productivity, and performance of your software projects and teams. Software analytics is the practice of collecting, analyzing, and visualizing data from software development processes and products to support decision making and improvement. In this article, you will learn about some of the most important software analytics methods for a software engineering manager, and how to apply them in your context.

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SHAKEEL ASIM

Entrepreneur | Architecting High-Performance Dev Teams | Crafting Innovative Software Solutions
Cosmin Rusu

Engineering Teams for Ambitions Founders | Founder @Duty Ventures | Ex {Google, Apple and Amazon}

1 Code metrics

Code metrics are quantitative measures of various aspects of your source code, such as size, complexity, cohesion, coupling, maintainability, readability, testability, and security. Code metrics can help you identify potential problems, risks, and opportunities for improvement in your code base, and track the progress and impact of your refactoring, testing, and debugging efforts. You can use various tools and frameworks to collect and visualize code metrics, such as CodeClimate , SonarQube , or CKJM .

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Karlo Sall

Lead Engineer @ The LEGO Group
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Previously I have introduced code metrics with the mindset of "boy scouting", when doing changes to the code, I will leave it in a better state than before. This could be added automated testing, following code conventions or the like. This has proven to be especially useful on old code bases or projects.

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Alexandre da Silveira

Diretor de Tecnologia | CTO | CIO | Conselheiro
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As métricas de código s?o essenciais para avaliarmos a qualidade do código que está sendo escrito. Consequentemente, a manutenabilidade por profissionais que nunca viram o código se torna mais segura.

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2 Process metrics

Process metrics are quantitative measures of how your software development process is performed, such as speed, efficiency, reliability, quality, and customer satisfaction. Process metrics can help you evaluate and optimize your software development lifecycle, from planning and design to delivery and deployment. You can use various tools and frameworks to collect and visualize process metrics, such as Jira , GitLab , or DORA .

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Karlo Sall

Lead Engineer @ The LEGO Group
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It's important to have insights on whats happening behind the process metrics. The team could have larger tasks across several sprints, which will have an impact on the burndown rate, there could be leave or the like.

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3 Product metrics

Product metrics are quantitative measures of how your software product is used, valued, and perceived by your customers and users, such as usage, retention, engagement, feedback, and loyalty. Product metrics can help you understand and improve your software product's functionality, usability, desirability, and market fit. You can use various tools and frameworks to collect and visualize product metrics, such as Google Analytics , Mixpanel , or Hotjar .

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Karlo Sall

Lead Engineer @ The LEGO Group
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Product metrics is a great opportunity to introduce A/B testing and feature toggles, where you both get control and metrics for each individual feature.

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4 Team metrics

Team metrics are quantitative measures of how your software engineering team is performing, collaborating, and growing, such as productivity, quality, efficiency, communication, morale, and learning. Team metrics can help you manage and motivate your software engineering team, foster a healthy and productive culture, and identify and address any issues or challenges. You can use various tools and frameworks to collect and visualize team metrics, such as GitHub , Slack , or Lattice .

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SHAKEEL ASIM

Entrepreneur | Architecting High-Performance Dev Teams | Crafting Innovative Software Solutions
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By leading teams in different projects, I've learned the importance of team metrics. These analytics provide insights into various aspects of team performance, such as productivity, quality, efficiency, and learning. By analyzing these analytics, a manager can make informed decisions, identify areas for improvement, and ensure that the team is delivering high-quality software in an efficient and effective manner.

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5 Business metrics

Business metrics are quantitative measures of how your software product is contributing to your business goals and objectives, such as revenue, profit, growth, market share, and customer lifetime value. Business metrics can help you align your software engineering strategy and decisions with your business vision and values, and demonstrate the value and impact of your software product to your stakeholders and investors. You can use various tools and frameworks to collect and visualize business metrics, such as Stripe , Baremetrics , or Tableau .

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Alexandre da Silveira

Diretor de Tecnologia | CTO | CIO | Conselheiro
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Observar métricas de negócio traz um senso de materializa??o do trabalho do time de engenharia, tangibilizado nos resultados do que foi construído. Para o time também aumenta o engajamento, pois dá a vis?o da "floresta"e n?o só da "minha árvore".

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6 Data-driven decisions

Data-driven decisions are decisions that are based on the analysis and interpretation of data, rather than on intuition, opinion, or experience. Data-driven decisions can help you improve your software engineering outcomes, reduce uncertainty and bias, and increase confidence and transparency. You can use various tools and frameworks to support data-driven decisions, such as A/B testing , Machine learning , or Dashboarding .

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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?

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Cosmin Rusu

Engineering Teams for Ambitions Founders | Founder @Duty Ventures | Ex {Google, Apple and Amazon}
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Employ sentiment analysis algorithms to seize the emotional tone and context surrounding your user experiences, team dynamics, or customer interactions. This supplements quantitative data, offering a better understanding of user satisfaction, team morale, or customer sentiments toward the product. And, you can also pair that with anomaly detection techniques to identify irregular patterns or irregularities in your software analytics data.

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What are the most important software analytics methods for a software engineering manager?

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2

3

4

5

6

7

1 Code metrics

2 Process metrics

3 Product metrics

4 Team metrics

5 Business metrics

6 Data-driven decisions

7 Here’s what else to consider

Software Development

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What are the most important software analytics methods for a software engineering manager?

1

2

3

4

5

6

7

1 Code metrics

2 Process metrics

3 Product metrics

4 Team metrics

5 Business metrics

6 Data-driven decisions

7 Here’s what else to consider

Software Development

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