Learn how to predict software risks early

Predict success or failure and pinpoin contributors

Request more information
Associated Products

Learn how to predict software risk level early.  Our risk identification and software risk prediction were from the world’s most comprehensive defect density benchmarking study.  Since 1993, we have been benchmarking almost 700 development factors against escaped defect density, defect removal efficiency, the technical debt slide, probability of on-time delivery, margin of error when the program is late, and customer satisfaction.  In this class, learn to identify one of 7 percentile rank clusters that indicate the overall program risk of success or failure.  

Software Project Risk Identification

Key Features 

You will learn how to predict software risk levels early and proactively:

  • Risk Identification: It provides a percentile rank for a project’s software risk, helping to visualize its risk profile relative to other projects.

  • Project Outcome: It predicts the likelihood of a project succeeding, failing, or languishing in mediocrity. This is a high-level forecast based on the analyzed factors.

  • Delivery and Technical Debt: It specifically predicts the probability of late delivery, the estimated lateness as a percentage of the original schedule, and the amount of technical debt that will accumulate and potentially impact future projects.

  • Pinpoint contributors: The contributors can be overlooked development practices as well as overkilled practices.  In many cases the software organization is implementing development practices before they are really ready.

Make data-driven decisions about where to allocate resources to reduce risks and ensure project success.

Predict software risk with facts

  • Built from world's largest software defect benchmarking

    More than 600 factors measured against the actual outcome of a project - on time delivery, technical debt, customer satisfaction. Predict the risk level with AI from actual historical data from mission and safety critical systems.

  • Software risk identification with machine learning

    The great thing about machine learning models - you don't know all of the answers to get a risk identification. No matter where you are in the program lifecycle you can still identify risk.

  • Requs AI Users

    Learn how to answer the Requs AI survey quickly and accurately. Learn how to improve the accuracy of the prediction. Note: Students do not have to have a license of Requs AI Predict Risk Identification to take this class.

Predict software risk early

Virtual Self Guided
$ 750
  • Learn about the benchmarking study and factors measures
  • Learn which software development factors matter the most and which factors are highly overrated
  • Learn the sensitivity of each of the factors and how to objectively measure them
  • Learn how to improve the confidence of the risk assessment when thing are in flux or you don't have all fo the information.
  • Class includes one copy of the "Cold Hard Truth About Software Defects".
  • Users of Requs AI Predict Risk Identification and Requs AI Optima inquire about discounts for training.
Purchase now
See terms and definitions
Everyone on the team can benefit when you predict software risks early

Software Test & QA

Identify and predict software risks early.  See how sensitive various test methods for fault injection without affecting the on time delivery.

 

Software Management

Learn the true contributors to both successful and failed programs.  Learn how to quickly identify a program that’s about to go off the rails.  Have quantitative evidence to justify the changes you want to make.  Learn how to eliminate practices that waste time.    

RAM engineers and site reliability

Learn how a high risk program translates directly into low availability and site reliability.  Identifying software risk the first step in the site reliability and availability predictions.

Software & Systems Safety

Understand the development factors that can contribute to unreliable software that inherently leads to unsafe software.