Analytics for Software Development

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This document summarizes a presentation by Ray Buse on software analytics. It discusses how analytics can provide insights through measurements, metrics, and analysis of software development data. Currently, data is plentiful but acting on it can be difficult. The goal is to make analytics more actionable and accessible by improving tools, user experience, data collection and ensuring high data quality while respecting privacy. Researchers analyze data but may lack project context, while managers have project knowledge but not analysis skills. Analysts can bridge this gap by having both analysis skills and project understanding to make the right assumptions and recommendations.

Technology Business

Thomas Zimmermann
tzimmer@microsoft.com
Ray Buse
buse@cs.virginia.edu
FoSER 2010 - Santa Fe, NM

11/7/2010 2 2Ray Buse • Analytics for Software Development

11/7/2010 3 3Ray Buse • Analytics for Software Development

11/7/2010 5 5Ray Buse • Analytics for Software Development
Analytics
Measurements
Metrics
Exploratory Analysis
Quantitative Analysis
Qualitative Analysis
Experiments
Insights
Insights
Why?
Measurements
What?
How much?
What if?
Goal
Analytics

6Ray Buse • Analytics for Software Development
Where is Software Eng. Today?
Few Insights Many Insights

7Ray Buse • Analytics for Software Development
Where is Software Eng. Today?

Data Rich
Telemetry
Architecture
Complexity
Documentation
Dependencies
Expertise
Code Clones
Velocity
Defect
Density
Readability
Churn
Ownership
Change Type
Test
Coverage
Failure
Models

Acting on data is hard
Researcher Developer
Researcher Manager
So what?
Readability
is 4.8
Complexity
is 9.6
…

Acting on data is hard
Researcher
Researcher Manager
Knows how to
analyze data
Makes
assumptions
about project
Knows project
very well
Has to learn about
analysis to understand
and act on results

Analytics Tools
Researcher Manager
Meets specific
information needs
Easy to use &
understand

Analyst
Software Analyst
Researcher
Researcher Manager
Knows enough about project to
make the right assumptions

Researcher
Researcher Manager
We need more
code reviews
Readability is 4.8
Analyst
This readability is a
problem for us

Make Data Actionable and Accessible
User experience
Data collection
Data quality
Privacy
Understand
user needs
Education

Thank you!
Tom Zimmermann
http://thomas-zimmermann.com
Ray Buse
http://arrestedcomputing.com

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The software defect factors are used to measure the quality of the software. The software effort estimation is used to measure the effort required for the software development process. The defect factor makes an impact on the software development effort. Software development and cost factors are also decided with reference to the defect and effort factors. The software defects are predicted with reference to the module information. Module link information are used in the effort estimation process. Data mining techniques are used in the software analysis process. Clustering techniques are used in the property grouping process. Rule mining methods are used to learn rules from clustered data values. The “WHERE” clustering scheme and “WHICH” rule mining scheme are used in the defect prediction and effort estimation process. The system uses the module information for the defect prediction and effort estimation process. The proposed system is designed to improve the defect prediction and effort estimation process. The Single Objective Genetic Algorithm (SOGA) is used in the clustering process. The rule learning operations are carried out sing the Apriori algorithm. The system improves the cluster accuracy levels. The defect prediction and effort estimation accuracy is also improved by the system. The system is developed using the Java language and Oracle relation database environment.

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Analytics for Software Development

1. Thomas Zimmermann tzimmer@microsoft.com Ray Buse buse@cs.virginia.edu FoSER 2010 - Santa Fe, NM

2. 11/7/2010 2 2Ray Buse • Analytics for Software Development

3. 11/7/2010 3 3Ray Buse • Analytics for Software Development

5. 11/7/2010 5 5Ray Buse • Analytics for Software Development Analytics Measurements Metrics Exploratory Analysis Quantitative Analysis Qualitative Analysis Experiments Insights Insights Why? Measurements What? How much? What if? Goal Analytics

6. 6Ray Buse • Analytics for Software Development Where is Software Eng. Today? Few Insights Many Insights

7. 7Ray Buse • Analytics for Software Development Where is Software Eng. Today?

8. Data Rich Telemetry Architecture Complexity Documentation Dependencies Expertise Code Clones Velocity Defect Density Readability Churn Ownership Change Type Test Coverage Failure Models

9. Researcher Manager Data is plentiful

10. Acting on data is hard Researcher Developer Researcher Manager So what? Readability is 4.8 Complexity is 9.6 …

11. Acting on data is hard Researcher Researcher Manager Knows how to analyze data Makes assumptions about project Knows project very well Has to learn about analysis to understand and act on results

12. Analytics Tools Researcher Manager Meets specific information needs Easy to use & understand

13. Analyst Software Analyst Researcher Researcher Manager Knows enough about project to make the right assumptions

14. Researcher Researcher Manager We need more code reviews Readability is 4.8 Analyst This readability is a problem for us

15. Make Data Actionable and Accessible User experience Data collection Data quality Privacy Understand user needs Education

16. Thank you! Tom Zimmermann http://thomas-zimmermann.com Ray Buse http://arrestedcomputing.com

Analytics for Software Development

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