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On the Accuracy of SonarQube
Technical Debt Remediation Time
Nyyti Saarimäki, Maria Teresa Baldassarre, Valentina Lenarduzzi, Simone Romano
EuroMicro SEAA, 28–30 August 2019
Problem
• Measuring technical debt has become more and more common
• Time to fix the TD is not accurate (practitioners)
29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 1
Technical Debt
• A sub-optimal solution
• Short term benefits
• Needs to be fixed
• Interest has to be paid
29.8.2019
Source: xkcd
On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 2
SonarQube
• Checks code against a set of rules.
• Rules:
•Type: Code smell, Bug, Vulnerability.
•Severity: Info, Minor, Major, Critical, Blocker
•Remediation time: estimated time to fix issues
| 329.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki
SonarQube
| 4
Rule Description Type Severity Remediation
Time (min)
S101 Class names should comply with a naming convention Code Smell Minor 5
S2096 “main” should not “throw” anything Code Smell Blocker 15
S1145 Useless “if(true” {…} and “if(false) {…}” blocks should
be removed
Bug Major 2
S2151 "runFinalizersOnExit" should not be called Bug Critical 20
S2039 Member variable visibility should be specified Vulnerability Minor 5
S2068 Credentials should not be hard-coded Vulnerability Blocker 30
29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki
| 529.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki
| 6
Remediation
time
29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki
Study Goals
•TD item remediation time accuracy
•Help companies, including SonarQube, to better estimate the
actual TD.
29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 7
Methodology
• Groups of 2-5 students
•Removed SQ issues from OSS projects
•Reach level A or reduce TD to a specified value
• Groups tracked the time needed to remediate TD items
29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 8
Methodology
• Groups of 2-5 students
•Removed SQ issues from OSS projects
•Reach level A or reduce TD to a value
• Groups tracked the time needed to remediate TD items
29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 9
Methodology
•Project Selection
•Written with Java
•Hosted on GitHub
•Size greater than 10KLOC
•SonarQube’s rating worse than “A” for at least two out of three
TD types
•Presence of a regression test suite
29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 10
Participants
| 11
• Students formed 15 groups
• 3rd-year undergraduate students (65)
•Background quite homogeneous (questionnaire)
•Close to graduating
• Attending the Software Quality course at University of Bari
•Course staff: Maria Teresa Baldassarre and Simone Romano
29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki
Estimation Error Evaluation
• RE: Relative Error, compares error with estimation
• MRE: absolute value of RE
• MmRE: Mean Magnitude Relative Error
• MdmRE: Median RE MdmRE
• MAR: Mean Absolute Residual
• PRED25: % of observations with MRE ≤ 0.25
• PRED50: % of observations with MRE ≤ 0.50
29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 12
RQ1: TD item diffuseness
• SQ monitors 1,568 unique rules
•243 were violated
• In total, projects contained 21,458 issues.
| 1429.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki
RQ1: TD item diffuseness
29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 15
RQ2: The accuracy of the TD remediation time
29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 17
RQ3: The accuracy of the TD remediation time
considering different types and levels of severity
| 2129.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki
RQ3: The accuracy of the TD remediation time
considering different types and levels of severity
| 2229.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki
Threats to Validity
• Remediation time
•Estimated > Actual
• Students refactored projects they had not developed before
• 7.6% of the total TD items removed
• 82% of the removed items were code smells
• Students were free to choose which TD items they wanted to remove
29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 26
Conclusion
• SonarQube remediation time tends to be overestimated
• Most accurate estimation: code smells
• Least accurate: bugs
• More empirical studies with a wider user-base will be needed to validate
our results.
29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 27
Questions?

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On the Accuracy of SonarQube Technical Debt Remediation Time - presentation slides

  • 1. On the Accuracy of SonarQube Technical Debt Remediation Time Nyyti Saarimäki, Maria Teresa Baldassarre, Valentina Lenarduzzi, Simone Romano EuroMicro SEAA, 28–30 August 2019
  • 2. Problem • Measuring technical debt has become more and more common • Time to fix the TD is not accurate (practitioners) 29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 1
  • 3. Technical Debt • A sub-optimal solution • Short term benefits • Needs to be fixed • Interest has to be paid 29.8.2019 Source: xkcd On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 2
  • 4. SonarQube • Checks code against a set of rules. • Rules: •Type: Code smell, Bug, Vulnerability. •Severity: Info, Minor, Major, Critical, Blocker •Remediation time: estimated time to fix issues | 329.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki
  • 5. SonarQube | 4 Rule Description Type Severity Remediation Time (min) S101 Class names should comply with a naming convention Code Smell Minor 5 S2096 “main” should not “throw” anything Code Smell Blocker 15 S1145 Useless “if(true” {…} and “if(false) {…}” blocks should be removed Bug Major 2 S2151 "runFinalizersOnExit" should not be called Bug Critical 20 S2039 Member variable visibility should be specified Vulnerability Minor 5 S2068 Credentials should not be hard-coded Vulnerability Blocker 30 29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki
  • 6. | 529.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki
  • 7. | 6 Remediation time 29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki
  • 8. Study Goals •TD item remediation time accuracy •Help companies, including SonarQube, to better estimate the actual TD. 29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 7
  • 9. Methodology • Groups of 2-5 students •Removed SQ issues from OSS projects •Reach level A or reduce TD to a specified value • Groups tracked the time needed to remediate TD items 29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 8
  • 10. Methodology • Groups of 2-5 students •Removed SQ issues from OSS projects •Reach level A or reduce TD to a value • Groups tracked the time needed to remediate TD items 29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 9
  • 11. Methodology •Project Selection •Written with Java •Hosted on GitHub •Size greater than 10KLOC •SonarQube’s rating worse than “A” for at least two out of three TD types •Presence of a regression test suite 29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 10
  • 12. Participants | 11 • Students formed 15 groups • 3rd-year undergraduate students (65) •Background quite homogeneous (questionnaire) •Close to graduating • Attending the Software Quality course at University of Bari •Course staff: Maria Teresa Baldassarre and Simone Romano 29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki
  • 13. Estimation Error Evaluation • RE: Relative Error, compares error with estimation • MRE: absolute value of RE • MmRE: Mean Magnitude Relative Error • MdmRE: Median RE MdmRE • MAR: Mean Absolute Residual • PRED25: % of observations with MRE ≤ 0.25 • PRED50: % of observations with MRE ≤ 0.50 29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 12
  • 14. RQ1: TD item diffuseness • SQ monitors 1,568 unique rules •243 were violated • In total, projects contained 21,458 issues. | 1429.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki
  • 15. RQ1: TD item diffuseness 29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 15
  • 16. RQ2: The accuracy of the TD remediation time 29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 17
  • 17. RQ3: The accuracy of the TD remediation time considering different types and levels of severity | 2129.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki
  • 18. RQ3: The accuracy of the TD remediation time considering different types and levels of severity | 2229.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki
  • 19. Threats to Validity • Remediation time •Estimated > Actual • Students refactored projects they had not developed before • 7.6% of the total TD items removed • 82% of the removed items were code smells • Students were free to choose which TD items they wanted to remove 29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 26
  • 20. Conclusion • SonarQube remediation time tends to be overestimated • Most accurate estimation: code smells • Least accurate: bugs • More empirical studies with a wider user-base will be needed to validate our results. 29.8.2019On the accuracy of SonarQube Technical Debt Remediation Time, @NyytiSaarimaki | 27