Produced in partnership with Brightspot
At its core, software engineering is focused on creating better, faster, and cheaper ways to automate tasks and get things done. By definition, one might assume software engineering is automatically efficient and effective. But what happens when the very people focused on developing software to make processes better, faster, and cheaper struggle to be effective and efficient?
Software engineering teams are by nature detail-oriented, innovative, and productive — yet according to Google’s 2024 DORA report, software developers are only able to spend 30-40% of their time actually writing code. Why?
Software engineering teams unnecessarily struggle due to several factors. First, there’s the inability to access the information they need when they need it, and once found, they have to verify if it’s up-to-date and accurate. This often leads to an endless series of Slack requests, phone calls, and meeting requests searching for answers. Additionally, engineers often lack insights into their processes due to communication barriers, data silos, and changing requirements. The fact that many organizations with thousands of developers struggle with the same issues only compounds these problems.
Software Engineering Insights: Improving performance with actionable engineering intelligence
These issues have driven the formation of an entirely separate solution category – Software Engineering Insights (SEI). SEI aggregates information from various sources, such as code repositories, issue trackers, testing tools, and performance monitoring systems, to provide tech leaders with actionable insights that inform key decisions and improve overall development efficiency and effectiveness.
This type of engineering intelligence is the foundation of creating a more effective software development lifecycle (SDLC). While incredibly valuable, it’s crucial to understand what it can and cannot tell you and recognize that these insights are only the first of many steps toward improving engineering effectiveness.
What information is available as a result of SEI analysis
Companies that decide to invest in analyzing their engineering processes and capabilities can gain valuable perspectives that guide strategic transformation. The following is a list of common key insights that can be derived from this analysis.
- Inefficiencies in your current SDLC: SEI can pinpoint bottlenecks in your SDLC. By analyzing metrics like lead time, cycle time, and deployment frequency, you can identify areas where improvements can be made. For example, if your deployment frequency is low, SEI might suggest where you can automate certain tasks or streamline your release process.
- Impact of technical debt: Technical debt, or the accumulation of suboptimal design and implementation choices, can significantly impact your development velocity and product quality. SEI can help you quantify the impact of technical debt by analyzing factors like code complexity, bug rates, and maintenance effort. By understanding the true cost of technical debt, you can prioritize refactoring efforts and allocate resources effectively.
- Effectiveness of your development practices: SEI can assess the effectiveness of your development practices, such as code reviews, testing, and continuous integration/continuous delivery (CI/CD). By analyzing metrics like code coverage, defect density, and build failure rates, you can identify areas where your practices may be falling short. For instance, if your code coverage is low, SEI might suggest investing in more robust testing strategies.
What information is not readily available via SEI analysis
As discussed above, SEI analysis can be a great starting point for businesses looking to improve their engineering effectiveness. Let’s see where it falls short and where additional mechanisms should be put in place.
- The root cause of complex issues: While SEI can identify trends and correlations, it may struggle to pinpoint the root cause of complex issues. For example, if your deployment frequency is low, SEI might identify a bottleneck in the deployment process. It may be unable, however, to determine the exact underlying reason for the bottleneck (e.g., insufficient testing, lack of automation, or organizational challenges). Consequently, you may spend unnecessary time automating tasks or changing organizational processes that aren’t directly related to the bottleneck.
- The “why” behind data: SEI can provide data-driven insights, but it can’t explain the “why” behind the data. For example, if SEI identifies a sudden increase in bug reports, it can’t automatically determine the cause (e.g., a recent code change, a new feature, or a change in the development environment). Without an understanding of the “why,” development teams may make the immediate changes suggested in the SEI analysis but miss the big picture and find themselves facing the same issues down the road.
- Subjective factors: SEI can’t measure subjective factors like team morale, developer satisfaction, or the quality of collaboration. These factors can significantly impact development velocity and product quality, but they are often difficult to quantify and analyze. For example, SEI might not be able to measure nuances related to team dynamics, communication, company culture, and the overall developer experience. These can be negatively affected by toolchain issues, poor documentation quality, inadequate leadership, etc., causing frustration and stifling innovation, making it necessary for companies to address them using additional improvement strategies.
One big subjective factor is the human element, such as team dynamics. SEI can’t directly measure team dynamics, collaboration, and communication, which are crucial for effective software development. Developer experience is another example. SEI might not capture the nuances of developer experience, such as toolchain issues, documentation quality, and overall satisfaction. Organizational culture is a third subjective factor. The organizational culture can significantly impact engineering effectiveness. SEI won’t directly assess the culture’s influence on collaboration, risk-taking, and innovation. Finally, leadership and management play a vital role in fostering a productive engineering environment. SEI won’t directly measure these factors.
- External factors: Factors not directly related to the development team, such as market demands, competition, and technological advancements, can influence engineering priorities and timelines. SEI might not fully account for these external influences. The same goes for regulatory compliance. Compliance with industry regulations and standards can impact engineering processes and resource allocation. While SEI analysis can assess your metrics, it’s usually unable to evaluate the impact of these regulations on your processes.
Laying the groundwork for transformative change
Due to the limitations discussed above, SEI alone is not enough to optimize your organization’s SDLC. Think of SEI as the first step in an evolving cycle of insights, improvements, iterations, and outputs to be used as the foundation for creating a more effective team. It provides foundational engineering intelligence upon which companies should further investigate, document, and remedy their people, processes, and outputs. SEI is a valuable tool for improving software development processes, but it does not paint the complete picture needed for lasting and truly impactful SDLC transformation. By understanding its capabilities and limitations, businesses can leverage SEI to make informed decisions and drive continuous improvement.
Let’s examine an example of what SEI analysis can (and can’t) tell a software development team experiencing increasing deployment failures.
| What SEI will reveal: | What SEI won’t reveal: |
| Increased error rates: SEI can quickly identify a spike in error rates during the deployment process. | The root cause of errors: While SEI can pinpoint the areas where errors are occurring, it may not be able to determine the underlying cause. For example, is it a coding error, a configuration issue, or a problem with the deployment pipeline? |
| Longer deployment times: It can highlight significant increases in deployment time, indicating potential bottlenecks. | Impact on user experience: SEI might not directly measure the impact of these failures on the end-user experience. It can’t quantify factors like user frustration or lost revenue. |
| Frequent rollbacks: SEI can detect a rise in the frequency of rollbacks, suggesting issues with code quality or deployment procedures. | Team morale and burnout: While SEI can measure productivity metrics, it can’t directly assess the emotional toll of frequent failures on the development team. |
Maximizing the impact of Software Engineering Insights
While there are limitations to SEI analysis, there are ways for teams to maximize the value of SEI. Development teams should combine SEI’s data-driven insights with informed developer perspectives and domain expertise. Here are some strategies:
- Correlate data with qualitative feedback: Combine quantitative data from SEI with qualitative feedback from developers and users to identify the root causes of issues.
- Prioritize improvements: Use SEI to identify and address the most critical areas for improvement, such as fixing high-impact bugs or optimizing slow-performing components.
- Set realistic goals: Establish clear and measurable goals for improvement, such as reducing deployment time by 20% or increasing code coverage to 90%. These goals will also help you decide which issues to address first.
- Continuously monitor and adapt: Regularly review SEI data to track progress and adjust your development processes as needed.
The next step after an initial SEI analysis is to engage in a broader Engineering Effectiveness study. A comprehensive Engineering Effectiveness analysis can help you identify and address root causes, subjective factors, and the “why” behind the data. By understanding both the strengths and limitations of engineering insights, teams can make more informed decisions, improve their development processes, and ultimately deliver higher-quality software by happier teams.
Ready to transform your engineering capabilities for more resilient results and sustainable innovation? Join our upcoming thought leadership webinar and explore how you can enhance developer experience and optimize software delivery with HTEC’s Engineering Effectiveness Framework.