Software reliability measurement: prediction, estimation, and assessment
Journal of Systems and Software - Special issue on the fifth Minnowbrook workshop on software performance evaluation
Projecting Software Defects from Analyzing Ada Designs
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
A Critique of Software Defect Prediction Models
IEEE Transactions on Software Engineering
Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
Agile software development
A Framework of Software Measurement
A Framework of Software Measurement
Software Metrics: A Rigorous and Practical Approach
Software Metrics: A Rigorous and Practical Approach
Agile Software Development with Scrum
Agile Software Development with Scrum
The Dangers of Using Software Metrics to (Mis) Manage
IT Professional
Status Report on Software Measurement
IEEE Software
Implementing a Software Metrics Program at Nokia
IEEE Software
MANAGER: Eight Secrets of Software Measurement
IEEE Software
Module Size Distribution and Defect Density
ISSRE '00 Proceedings of the 11th International Symposium on Software Reliability Engineering
An Empirical Study of Software Reuse vs. Defect-Density and Stability
Proceedings of the 26th International Conference on Software Engineering
Empirical evaluation of defect projection models for widely-deployed production software systems
Proceedings of the 12th ACM SIGSOFT twelfth international symposium on Foundations of software engineering
Characterizing a data model for software measurement
Journal of Systems and Software - Special issue: The new context for software engineering education and training
Use of relative code churn measures to predict system defect density
Proceedings of the 27th international conference on Software engineering
Static analysis tools as early indicators of pre-release defect density
Proceedings of the 27th international conference on Software engineering
Extreme Programming Explained: Embrace Change (2nd Edition)
Extreme Programming Explained: Embrace Change (2nd Edition)
Proceedings of the 28th international conference on Software engineering
Software Measurement and Estimation: A Practical Approach (Quantitative Software Engineering Series)
Software Measurement and Estimation: A Practical Approach (Quantitative Software Engineering Series)
Implementing Lean Software Development: From Concept to Cash (The Addison-Wesley Signature Series)
Implementing Lean Software Development: From Concept to Cash (The Addison-Wesley Signature Series)
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
Measuring where it matters: Determining starting points for metrics collection
Journal of Systems and Software
From Traditional to Streamline Development — opportunities and challenges
Software Process: Improvement and Practice - Special Issue on Systems Interoperability
Predicting weekly defect inflow in large software projects based on project planning and test status
Information and Software Technology
A framework for developing measurement systems and its industrial evaluation
Information and Software Technology
Software Reliability and Metrics
Software Reliability and Metrics
Predicting short-term defect inflow in large software projects: an initial evaluation
EASE'07 Proceedings of the 11th international conference on Evaluation and Assessment in Software Engineering
PROFES'11 Proceedings of the 12th international conference on Product-focused software process improvement
Information and Software Technology
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Context: Predicting a number of defects to be resolved in large software projects (defect backlog) usually requires complex statistical methods and thus is hard to use on a daily basis by practitioners in industry. Making predictions in simpler and more robust way is often required by practitioners in software engineering industry. Objective: The objective of this paper is to present a simple and reliable method for forecasting the level of defect backlog in large, lean-based software development projects. Method: The new method was created as part of an action research project conducted at Ericsson. In order to create the method we have evaluated multivariate linear regression, expert estimations and analogy-based predictions w.r.t. their accuracy and ease-of-use in industry. We have also evaluated the new method in a life project at one of the units of Ericsson during a period of 21weeks (from the beginning of the project until the release of the product). Results: The method for forecasting the level of defect backlog uses an indicator of the trend (an arrow) as a basis to forecast the level of defect backlog. Forecasts are based on moving average which combined with the current level of defect backlog was found to be the best prediction method (Mean Magnitude of Relative Error of 16%) for the level of future defect backlog. Conclusion: We have found that ease-of-use and accuracy are the main aspects for practitioners who use predictions in their work. In this paper it is concluded that using the simple moving average provides a sufficiently-good accuracy (much appreciated by practitioners involved in the study). We also conclude that using the indicator (forecasting the trend) instead of the absolute number of defects in the backlog increases the confidence in our method compared to our previous attempts (regression, analogy-based, and expert estimates).