Workshop Template - University of Wisconsin-Platteville
Advancing Requirements-Based Testing Models to Reduce Software Defects Craig Hale, Process Improvement Manager and Presenter Mara Brunner, B&M Lead Mike Rowe, Principal Engineer Esterline Control Systems - AVISTA Software Requirements-Based Testing Defect Model Focus: requirements-based test (RBT) reviews Quality imperative, but cost impacts Large amount of historical data
Model: defects per review based on number of requirements Suspected review size a factor Used for every review Looked at controllable factors to improve reviews effectiveness Stakeholders: Customers Project leads and engineers Baselines and models team Model Goals
Improve overall quality of safety-critical systems Focus on improving review process Maximize defect detection rate Minimize defect escapes Reduce defect injection rate Reduce cost of poor quality Defect process performance baselines split Application type avionics, medical, etc. Embedded vs. non Complexity level
Factors 2011 Metrics 738 reviews over three years 19,201 requirements Customers: 10, projects: 21, jobs: 36 2012 Metrics 337 reviews over one year 2,940 requirements Customers: 5, projects: 7, jobs: 11 Y Variables
Number of defects per review (D/R) discrete: ratio data type Defects per requirement (D/Rq) continuous: ratio data type Predicted Outcomes Expected defects in the review per number of requirements Important to understand if exceeding expected defects Valuable to understand if all defects were detected
Inverse relationship of defects/requirement detected and review size Modeling Techniques Non-linear regression vs. linear regression vs. power function Standard of error estimate varied considerably Partitioned into nine intervals Monte Carlo simulation Standard of error estimate did not change by more than 0.000001 for ten iterations Determined standard of error estimate for each partition
Factors and Correlation Tables D = Defects PT = Preparation Time R = Review Rq = Requirement Data Collection: Requirements Count 2011 Data Collection: Partitioning of Reviews 2011 Output from Model 2011
4 Requirements 20 Requirements Pilot Results 2011 Determined to automate model Needed statistical formula for variance More guidance on what to do when out of range Project
Organization Mean Standard Deviation Mean Standard Deviation
Review Size -7.17% +209.9% -46.24% -67.62% Defects Per
-13.55% -16.71% -7.09% -15.13% Results, Benefits and Challenges Points to decreasing variation in defects Provides early indicator to fix processes and reduce defect injection rate
Indicates benefits for small reviews and grouping Challenged with gaining buy-in, training and keeping it simple Hypothesis Test for Defects/Rqmt and Review Size Reviews June 2011 and Later May 2011 and Earlier Defects/Rqmt Mean Review Size
mean 0.3898 8.7226 sd 0.9387 24.4248
N 337 mean 0.2484 26.4241 sd
1.3168 52.8535 N 738 t Hypothesis Test df
p (2-tailed) < % Mean Differences 2.0061 -7.5102 1073 1073
0.0450 0.0000 56.89% -66.99% Potential New Model Element Years of Experience Purpose: Investigate the relationship between a reviewers years of experience and the quality of reviews that they
perform Expected Results: Engineers with more experience would be better reviewers Factors: Data studied from 1-Jun-2011 through 25-May-2012 337 internal reviews 11 jobs 7 projects
5 different customers Data Collection: Requirements Count Data Collection: Defects per Review Data Collection: Review Prep Time per Review Data Collection: Review Prep Time per Rqmt per Defect Potential New Model Element Years of Experience Findings:
Analyzed trend between the independent variable and total years of experience The review process showed stability with no significant impact per years of experience Summary What worked well Utilizing historical data to predict outcomes Encouragement of smaller data item reviews Improving the defect detection rate of data item reviews Future plans: Continue to enhance the model
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