How Expected Value (EV) Can Be Used in Software Development

In software development, decision-making often involves balancing uncertain outcomes, prioritizing features, and managing limited resources. This is where Expected Value (EV) comes into play. EV, a concept from probability theory, helps teams make data-driven decisions by calculating the average outcome of potential scenarios. By using EV in software development, teams can weigh risks, rewards, and the likelihood of success to make more informed choices.

Let’s explore how Expected Value can be applied to different stages of software development, from feature prioritization to managing technical debt and risk assessment.

What is Expected Value (EV)?

Expected Value (EV) represents the average result of a decision when considering all possible outcomes and their probabilities. It’s calculated by multiplying each possible outcome by its likelihood, then summing those values. In essence, EV provides insight into what the likely result would be if the decision were made repeatedly over time.

For example, if a feature has a 50% chance of generating $100,000 in revenue and a 50% chance of costing $20,000 in development time, the expected value would be:

EV = (50% * $100,000) + (50% * -$20,000) = $50,000 - $10,000 = $40,000

This helps software development teams understand the average outcome of different decisions, especially when weighing feature development, bug fixes, or technical debt management.

Applying Expected Value in Software Development

Here’s how Expected Value can be applied throughout the software development process:

Feature Prioritization

In an Agile or Lean software development environment, prioritizing features effectively is crucial. Not all features provide the same business value, and resources are limited. Using EV, teams can prioritize features based on their potential impact, considering both the benefits (increased customer satisfaction, revenue generation) and the costs (development time, complexity, risk of bugs).

For example:

• Feature A may have a 70% chance of increasing revenue by $200,000 but will take 3 months to develop.
• Feature B may have a 90% chance of improving customer satisfaction, reducing churn, but will take 1 month to develop.

By calculating the expected value of each feature (based on time, cost, and potential reward), teams can make more informed decisions on which features will provide the most impact over time.

Risk Management

Every software project comes with risks—be it delays, bugs, or security vulnerabilities. By calculating the expected value of different risks, development teams can proactively manage their impact. For instance, when evaluating whether to implement a security patch, teams can assess the probability of a breach occurring if the patch is not applied and weigh that against the costs (both monetary and reputational) if a breach were to happen.

• If the likelihood of a security breach is 10%, but the cost of the breach is $1,000,000, then the expected value of not applying the patch is $100,000.

Knowing this, teams can better decide when and where to invest resources in risk mitigation.

Managing Technical Debt

Technical debt refers to shortcuts or compromises made during development that may need to be corrected later. Often, there’s a balance between addressing technical debt now or delaying to focus on new features. EV helps assess whether addressing the debt now will lead to greater long-term benefits.

If the team knows that delaying fixing a certain bug or refactoring a part of the code will slow development and increase future costs, they can calculate the expected value of paying off the debt now versus delaying it.

For example:

• Fixing a bug now will take 2 weeks but could reduce future development time by 10%.
• The expected future cost of not fixing it could be $50,000 in delays, while the current cost is $10,000.

EV helps software teams choose when it’s worth paying down technical debt and when it’s smarter to focus on delivering new features.

Resource Allocation

In software development, resources like time and talent are often spread thin. EV can help teams make strategic decisions about where to allocate these resources by identifying which tasks will have the greatest long-term impact. Whether it’s allocating developers to a specific project or determining which features should get the most attention, EV provides a framework to ensure the team’s efforts are invested in the highest-value areas.

A/B Testing and Decision Making

EV is especially useful in A/B testing, where teams test different variations of a feature to see which performs better. By assigning probabilities to each version’s success and calculating the expected value of each outcome, software teams can optimize their decisions.

For example, if Version A has a 60% chance of improving user engagement by 20% and Version B has an 80% chance of improving engagement by 10%, EV can help determine which version to implement.

By using expected value, teams can go beyond just picking the version that wins the A/B test and start making decisions based on the long-term value that different variations provide.

Release Planning

When planning releases, product teams often have to balance multiple priorities: new features, bug fixes, performance improvements, and customer requests. EV helps align release plans with business objectives by focusing on what delivers the most value over time. Instead of being reactive—fixing the loudest issues or implementing features that “seem” important—teams can evaluate potential outcomes and make release decisions based on the long-term expected value for the company and its users.

Expected Value Helps Drive Data-Driven Decisions in Software Development

In software development, making decisions based on gut instinct or short-term gains can be risky. Expected Value helps guide those decisions by providing a clear, data-driven way to evaluate choices, prioritize tasks, and allocate resources more effectively.

By applying EV in software development, teams can move beyond immediate reactions and focus on the bigger picture—ensuring that every decision, from feature prioritization to resource allocation, contributes to long-term success.

In short, Expected Value is a powerful tool for:

Prioritizing features based on their impact vs. cost.

• Managing risks by calculating the potential outcomes of different scenarios.
• Deciding when to tackle technical debt versus building new features.
• Allocating resources in ways that maximize long-term value.
• Improving decision-making in A/B testing and release planning.

EV doesn’t just help teams make better decisions; it helps them make smarter decisions—ones that balance risk, reward, and the future growth of the software.