Predict Software Risk Before It Becomes an Expensive Failure.
Predict defects, RAM performance, reliability, availability, edge cases, and software failure modes—often before the first line of code is written.
Requs AI gives engineering organizations earlier visibility into the software risks that threaten mission success, safety, reliability, schedule, budget, and contractual performance. Instead of waiting for integration, testing, or field failures to expose problems, Requs AI helps teams identify risk during requirements, architecture, planning, and design—when there is still time to change the outcome.
See how Requs AI can be applied to your program in a personalized technical walkthrough.
Turn Software Reliability from a Gamble into a Science.
Built on decades of software failure research.
Requs AI is powered by Mission Ready Software's extensive software-failure research and benchmarking—giving engineering teams a data-driven way to understand what drives reliable and available software.
Research includes a benchmarking study of 679 factors affecting reliable and available software.
Decades studying successful and failed software programs and the root causes behind software failure.
Common Defect Enumeration research identifies pre-coding defect origins representing roughly 70% of defects in its root-cause analysis.
Find the risk while it is still a design decision.
Most Software Risk Is Discovered Too Late.
Today's systems are increasingly software-defined. At the same time, engineering organizations are being asked to deliver faster while meeting harder requirements.
Many critical software risks are not discovered until integration, testing, deployment, or operation. By then, the organization may already be dealing with:
Late Defect Discovery
Critical problems surface when correcting them is more expensive, disruptive, and time consuming.
Reliability & Availability Surprises
Programs discover too late that software may not achieve required reliability, uptime, or RAM objectives.
Hidden Edge Cases
Unexpected combinations of states, timing, sequences, sensor inputs, interfaces, and error conditions create unanticipated failures.
Software FMEA Bottlenecks
Traditional FMEA processes struggle to keep pace with Agile development and rapidly changing designs.
Schedule & Budget Pressure
Late rework consumes engineering resources, delays milestones, reduces margins, and threatens contractual performance.
Requs AI changes when engineering teams receive the information.
Don't wait for the failure to find the risk.
Don't Just Shift Left. Start to the Left.
Describe the System
Provide characteristics such as:
Requs AI Identifies the Risk
Requs AI applies predictive models, benchmarking, software-failure research, and Common Defect Enumerations to determine which risks and failure patterns are most applicable to the system.
BENCHMARKING DATA
FAILURE RESEARCH
COMMON DEFECT ENUMERATIONS
Your Engineering Team Acts
Find the problem while it's still a design decision—not a field failure.
One Platform. Multiple Views of Software Risk.
From quantitative predictions to hidden failure modes, Requs AI gives engineering teams actionable intelligence across the software lifecycle.
Requs AI Predict
Know where the program is headed.
RAM Prediction
Know whether reliability targets are achievable.
Edge Case Prediction
Find the failure scenarios nobody thought to test.
Software FMEA
Turn edge cases into actionable failure analysis.
Requs AI Optima
Find a better path before the program runs off track.
Requs AI Trend
Know when you've tested enough.
Forecast failures from active testing and determine how much additional testing and defect correction may be required to achieve reliability and availability objectives.
Explore Requs AI Trend →Requs AI Causality
Turn Jira defect history into root-cause intelligence.
Use NLP and machine learning to analyze defect reports and categorize recurring root causes using Common Defect Enumerations.
Explore Requs AI Causality →Requs AI Blueprint
Start with industry-specific failure intelligence.
Accelerate Edge Case Prediction and Software FMEA with prepackaged edge cases, failure modes, test cases, and design controls tailored to your industry.
Explore Requs AI Blueprint →Which Requs AI capability fits your program?
Tell us what you're developing, what reliability requirements you need to meet, and where you need greater visibility.
Built for the Teams Accountable for Mission Success.
See the trajectory before the program gets off course.
Software FMEA Built for Software.
Software doesn't fail like hardware.
Software failures can emerge from combinations of states, modes, timing, sequences, data flow, interfaces, error handling, software transitions, hardware interaction, and human interaction. Traditional hardware-oriented FMEA methods may not adequately expose these software-specific root causes.
Requs AI's 6D CDE Software FMEA approach begins with Common Defect Enumerations and identifies the edge cases most relevant to the product—then connects those conditions to effects, mission failures, safety hazards, controls, and test strategy. The process supports MBSE integration and can generate outputs including critical-item and SSHA worksheets.
AI-Assisted Analysis
Requs AI identifies relevant edge cases and performs the heavy upfront analysis—current materials cite 67% of the work.
Agile Speed
Designed to align Software FMEA with Agile development cycles.
MBSE Integration
Use existing system-model information to strengthen and accelerate analysis.
Safety & Mission Focus
Connect software root causes to the hazards and mission impacts that matter.
Your Source Code Stays Yours.
Requs AI does not need to access or analyze your source code.
For aerospace, defense, government, energy, and other sensitive engineering environments, protecting proprietary information is critical. Requs AI can analyze the logical skeleton of the software system—the states, modes, sequences, timing dependencies, interfaces, data flow, and error-handling characteristics—without requiring customer source code or control algorithms.
Your proprietary algorithms and code remain under your control.
Requs AI evaluates system characteristics rather than proprietary implementation.
Keep Requs AI inside the customer's controlled environment.
Deployment options for highly secure environments without normal internet connectivity.
Integrate engineering model information into applicable Requs AI workflows.
Built on Decades of Understanding Why Software Fails.
Software reliability is our mission. Requs AI is the culmination of decades of software-failure analysis, reliability research, benchmarking, and experience with successful and failed software programs.
How the World’s Largest Database of Software Failures Was Created
Research Begins
Ann Marie pioneered a large-scale effort to identify overrated practices and undervalued practices to support fact-based decision-making.
Data Collection
Ann Marie benchmarked hundreds of development factors at hundreds of engineering companies against the key outcomes — escaped defect density, probability of on-time delivery, and margin of error on delivery schedule.
Countless Failures With Common Root Causes
By this point, Ann Marie had analyzed tens of thousands of defects by root cause. Surprisingly, all of them shrank down to only a few hundred unique causes. More importantly, she figured out how to spot these without digging through code.
The First ML Defect Prediction Model
Decades of research culminated in Requs AI Predict in 2022, which predicts risk and defect density before the code is written. It has more data, factors, and accuracy than previous regression models.
The First ML Edge Case/FMEA Tool
In 2023, Requs AI Edge Case Prediction and Software FMEA was deployed, which predicts edge cases before the code is written.
Organizations we’ve helped mission-proof
Coverage of our work in software reliability
Grounded in Recognized Software Reliability & Safety Standards.
Requs AI is built on the same frameworks practitioners use to analyze, predict, and mitigate software failure. Explore the standards and guidance documents that inform our approach.
When Software Failure Isn't an Option.
See What Requs AI Finds Before Your Software Fails.
Predict Earlier. Prevent More. Be Mission Ready.
Schedule a Software WalkthroughSoftware Reliability Prediction & Software FMEA, Answered
What is software reliability prediction?
Software reliability prediction estimates how many defects a release will contain, when they will be found, and how they affect availability — before the code is written. Requs AI applies IEEE 1633 prediction models to your requirements, process data and development practices to produce defect counts, a defect discovery profile and RAM figures early enough to change the outcome.
How is Software FMEA different from hardware FMEA?
Software FMEA analyses failure modes caused by requirements, logic, data, timing, states and interfaces — not physical wear-out. Requs AI uses the Common Defect Enumeration (CDE) and the 6D CDE process aligned with IEEE 1633 and SAE J1025, so software failure modes are enumerated systematically instead of brainstormed.
Which standards does Requs AI support?
IEEE 1633 (Recommended Practice for Software Reliability) and SAE J1025 (software FMEA), plus reliability program expectations in safety and mission standards — including software fault tree analysis, software allocation in the system reliability model, and software failures in FRACAS.
Can software defects really be predicted before coding starts?
Yes. Defect density and failure modes correlate strongly with measurable program characteristics: requirements quality, staffing, process maturity, complexity and test strategy. Requs AI scores those inputs against decades of industry failure data to predict defect counts, edge cases and reliability growth before the first line of code.
What are edge cases, and why do they cause field failures?
Edge cases are the inputs, states, timings and sequences at the boundary of what a system was designed to handle. Most escaped field failures trace back to edge cases never enumerated in requirements or tests. Requs AI identifies the most common edge cases by function type so they can be designed for and verified.
Who uses Requs AI?
Reliability engineers, systems engineers, software leads, quality managers and program managers across aerospace, defense, medical devices, automotive, energy and industrial manufacturing — wherever software failure carries safety, mission or warranty cost.
Earlier Visibility Creates Better Decisions.
The earlier engineering teams understand software risk, the more options they have to change the outcome.
What Could Requs AI Identify in Your Software Program?
Tell us what you're building, where reliability matters, and what you're trying to predict. We'll show you how Requs AI can help your team identify software risk, predict defects and RAM performance, uncover edge cases, strengthen Software FMEA, and make better decisions earlier in development.
A focused conversation around your program. No generic sales presentation.