How To Identify Technical Debt In Java (Tty) On A Microsoft Powerbook (V0.2.2) On An Ipa (Microsoft) Microsoft Microsoft (Powerbook) On Microsoft.Com (V1

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1 visualization and representation for scientific analysis Seminar Foundations in Empirical Software Engineering Dominik Münch Institut für Informatik Software & Systems Engineering

2 ization The use of computer-supported, interactive, visual representations of abstract data to amplify cognition. [1] 2

3 ization Approaches Exploratory Directed?? adapted from [2] 3

4 Analysis Process Form Raw Tables Structures s Collection Transformations Mappings Transformations Human Interaction adapted from [1] 4

5 Raw Acquired via qualitative & quantitative methods Idiosyncratic formats Transformed into relations {<Valueix, Valueiy, >, <Valuejx, Valuejy, >, } M. Kumaraswamy, 13. Nov 2014 Example Study on Technical Debt (TD) identification Hadoop repository (v0.2.0 to v0.14.0) 96,720 data points Raw Transformations Tables Mappings Structures Transformations s 5

6 Technial Debt Indicators Modularity Violations Presence of Modularity Violation [0,1] Grime Code Smells ASA Issues Presence of Grime [0,1] Absence of Design Pattern [0,1] God Class [0,1] Brain Class [0,1] Class Level Code Smells Refused Parent Bequest [0,1] Tradition Breaker [0,1] Feature Envy [0,1] Class [0,1] Brain Method Method Level Code Smells Intensive Coupling Dispersed Coupling Shotgun Surgery High By Priority Medium Low Bad Practice Correctness I18N (Internationalization) By Category Malicious Code Multi Thread (MT) Correctness Performance Security Style Other Metrics Size Number of Methods Software Quality Metrics Defect Proneness Change Proneness Number of bug fixes affecting this version Number of bug fixes fixed in this version Number of bug fixes counting between affected an d fixed this version Change Likelihood [ ] Raw Transformations Tables Mappings Structures Transformations s 6

7 Raw Transformations Tables Mappings Structures Transformations s 7

8 Tables Case Casei Casej Casek Variablex Valueix Valuejx Valuekx Variabley Valueiy Valuejy Valueky Combine relations with metadata Show dimensionality (number of variables) Can describe hierarchical and network data Raw Transformations Tables Mappings Structures Transformations s 8

9 Transformations Values Derived Values (1) Values Derived Structure (2) Structure Derived Structure (3) Structure Derived Values (4) (3) File A B C File A B C File A B C File A C B Number of Methods Number of Methods Number of Methods Number of Methods FindBugs (Low) FindBugs (Medium) FindBugs (Total) (1) FindBugs FindBugs FindBugs FindBugs FindBugs (High) FindBugs > (2, 3) FindBugs > Raw Transformations Tables Mappings Structures Transformations s 9

10 File Dispersed Performance Bug fixes FileCoupling Dispersed Performance (between) Bug fixes FileCoupling Dispersed Performance (between) Bug fixes A 1 2 (between) 7 A B A B B Dispersed TD TD Indicator Indicator Dispersed Performance Performance Coupling TD IndicatorCoupling Dispersed Performance Bug Coupling fixes Bug fixes Interest Interest Indicator Indicator Bug (between) fixes Bug (between) fixes Interest Indicator(between) Bug fixes (between) Bug fixes Association (between) 0.6 (between) 0.1 Association Association Metrics per java class (per version) Association between metrics (per version) TD Indicator Interest Indicator Dispersed Coupling Bug fixes (between) Performance Bug fixes (between) Overall Association Overall association between metrics Raw Transformations Tables Mappings Structures Transformations s 10

11 Sheet 1 Interest Indicator Value Sheet 1 TD Indicator Bug fixes (between) TD Indicator Brain Class Brain Method Correctness Dispersed Coupling Feature Envy God Class High Intensive Coupling Modularity violations MT Correctness Number of Methods Performance Shotgun Surgery Style Tradition breaker Bug fixes (between) Bug fixes (fixed) Bug fixes (inject) Change likelihood Value broken down by Interest Value Indicator vs. TD Indicator. Color shows details about Value. The marks are labeled by Value. Interest Indicator Bug fixes (fixed) Bug fixes (inject) Change likelihood Brain Class Raw Transformations Brain Method Tables Mappings Structures Transformations s

12 Structures [ ] augment a spatial substrate with marks and graphical properties to encode information. [1] expressive represents all and only the data from the Table effective is faster to interpret, can convey more distinctions or leads to fewer errors than other mappings Raw Transformations Tables Mappings Structures Transformations s 12

13 Number of Methods, Class Sheet 8 Full Class Name 30 Number of methods in class NOT EXPRESSIVE! 0.dfs.nodeProtocol.java.dfs.DFSClient.java.dfs.DFSFileInfo.java.dfs.FSDirectory.java.dfs.NameNode.java.fs.FSOutputStream.java.io.BytesWritable.java.io.OutputBuffer.java.io.IntWritable.java.io.LongWritable.java.io.WritableComparable.java.io.WritableComparator.java The trend of sum of N Methods for Full Class Name. The data is filtered on Version, which keeps Raw Transformations The view is filtered Tables on Full Class Mappings Name, which keeps Structures 49 of 391 Transformations members. s 13

14 Number of Code Smells, Version NOT EFFECTIVE! Number of code smells Raw Transformations Tables Mappings Structures Transformations s 14

15 Total Number of Code Smells, Version Total number of code smells Version Code smells in the last release (352) are more than twofold the number of code smells in the first release (143), [ ] [3] Total Number of Code Smells Total Number of Code Smells for each Version broken down by Total Number of Code Smells. The view is filtered on Version, which keeps Transformations 15 Raw Tables Mappings Structures Transformations and s

16 Modularity Violations, Change Likelihood Modularity Violation Modularity violations are strongly associated with change proneness. [3] Modularity Violation, Change Likelihood No Yes 0,00 0,02 0,04 0,06 0,08 0,10 0,12 0,14 0,16 0,18 0,20 Change Likelihood Change Likelihood for each Modularity Violation. Details are shown for Full Class Name. The view is filtered on Change Likelihood, which ranges from to Raw Transformations Tables Mappings Structures Transformations s 16

17 Transformations Modularity Violation Filtering Highlighting Aggregation Hierarchical Navigation Modularity Violation, Change Likelihood No Version Yes 0,00 0,02 0,04 0,06 0,08 0,10 0,12 0,14 0,16 0,18 0,20 Change Likelihood Change Likelihood for each Modularity Violation. Color shows details about Version. The view is filtered on Change Likelihood and Version. The Change Likelihood filter ranges from to The Version filter keeps , , , and Raw Transformations Tables Mappings Structures Transformations s 17

18 Analysis Process Form Raw Tables Structures s Collection Transformations Mappings Transformations Human Interaction 18

19 Tools Tableau ( R ( d3.js ( Google Charts ( ManyEyes ( RAW ( Lyra ( 19

20 Tips Know your data! Document what you do! 3D is usually not a good idea (occlusion, perspective foreshortening) KISS (Keep it simple and stupid) 20

21 Numbers have an important story to tell. They rely on you to give them a clear and convincing voice. Stephen Few 21

22 References 1.Card, Stuart K., Mackinlay, Jock D., Shneiderman, Ben, eds. Readings in Information ization: Using Vision to Think. Morgan Kaufmann, Few, Stephen, Now you see it: Simple ization Techniques for Quantitative Analysis. Analytics Press, Zazworka, Nico, et al. "Comparing Four Approaches for Technical Debt Identification." Software Quality Journal (2013):