Changes and Bugs: Mining and Predicting Development Activities

CHANGES and BUGS Mining and Predicting Software Development Activities Thomas Zimmermann

Software development Build

Collaboration Comm. Version Bug Archive Archive Database Mining Software Archives

MY THESIS . additions analysis architecture archives aspects bug cached calls changes collaboration complexities component concerns cross- cutting cvs data defects design development drawing dynamine eclipse effort evolves failures fine-grained fix fix-inducing graphs hatari history locate matching method mining predicting program programmers report repositories revision software support system taking transactions version visualizing

Contributions of the thesis Fine-grained analysis of version archives. 1 Project-specific usage patterns of methods (FSE 2005) Identification of cross-cutting changes (ASE 2006) Mining bug databases to predict defects. 2 Dependencies predict defects (ISSRE 2007, ICSE 2008) Domino effect: depending on defect-prone binaries increases the chances of having defects (Software Evolution 2008).

Fine-grained analysis public void createPartControl(Composite parent) { ... // add listener for editor page activation getSite().getPage().addPartListener(partListener); } public void dispose() { ... getSite().getPage().removePartListener(partListener); }

Fine-grained analysis public void createPartControl(Composite parent) { ... // add listener for editor page activation getSite().getPage().addPartListener(partListener); } public void dispose() { co-added ... getSite().getPage().removePartListener(partListener); }

Fine-grained analysis public void createPartControl(Composite parent) { ... close // add listener for editor page activation open getSite().getPage().addPartListener(partListener); println } public void dispose() { co-added ... getSite().getPage().removePartListener(partListener); } begin Co-added items = patterns

Fine-grained analysis public static final native void _XFree(int address); public static final void XFree(int /*long*/ address) { lock.lock(); try { _XFree(address); } finally { lock.unlock(); } } D IN N GE I O N S CHA CAT 1284 LO Crosscutting changes = aspect candidates

Bugs! Bugs! Bugs!

Quality assurance is limited... ...by time... ...and by money.

Spent resources on the components that need it most, i.e., are most likely to fail.

Indicators of defects Code complexity Code churn Complex Code is more Changes are likely to prone to defects. introduce new defects. History Dependencies Code with past defects is Using compiler packages more likely to have future is more difficult than using defects, packages for UI.

2252 Binaries 28.3 MLOC

Hypotheses Complexity of dependency graphs Sub system correlates with the number of post-release defects (H1) level can predict the number of post-release defects (H2) Network measures on dependency graphs Binary correlate with the number of post-release defects (H3) level can predict the number of post-release defects (H4) can indicate critical “escrow” binaries (H5)

DATA. .

Data collection six months Release point for to collect Windows Server 2003 defects Dependencies Network Measures Complexity Metrics Defects

Centrality Degree Closeness Betweenness Blue binary has dependencies Blue binary is close to all other Blue binary connects the left to many other binaries binaries (only two steps) with the right graph (bridge)

Centrality • Degreethe number dependencies centrality - counts • Closeness centrality binaries into account - takes distance to all other - Closeness: How close are the other binaries? - Reach: How many binaries can be reached (weighted)? - Eigenvector: similar to Pagerank • Betweenness centrality paths through a binary - counts the number of shortest

Complexity metrics Group Metrics Aggregation Module metrics # functions in B for a binary B # global variables in B # executable lines in f() # parameters in f() Per-function metrics Total # functions calling f() for a function f() Max # functions called by f() McCabe’s cyclomatic complexity of f() # methods in C # subclasses of C OO metrics Total Depth of C in the inheritance tree for a class C Max Coupling between classes Cyclic coupling between classes

RESULTS. .

Prediction Input metrics and measures Model Prediction PCA Regression Metrics Classiﬁcation SNA Metrics+SNA Ranking

Classiﬁcation Has a binary a defect or not? or

Ranking Which binaries have the most defects? or or ... or

Random splits 4×50×

Classiﬁcation (logistic regression)

Classiﬁcation (logistic regression) SNA increases the recall by 0.10 (at p=0.01) while precision remains comparable.

Ranking (linear regression) SNA+METRICS increases the correlation by 0.10 (signiﬁcant at p=0.01)

FUTURE WORK . bug cached calls bug changes collaboration additions analysis architecture archives aspects analysis archives aspects changes collaboration complexities component concerns cross- complexities component concerns cross-cutting cvs data defects cutting cvs data defects design development drawing dynamine design development drawing eclipse erose evolves factor eclipse effort evolvesfix-inducing fine-grained fix fix-inducing failures fine-grained fix failures fm graphs guide hatari graphs hatari history locate matching method mining history human matching mining networking predicting program programmers report repositories predicting program programmers system report repositories revision software support quality taking transactions revision social software support system taking version version visualizing

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Changes and Bugs: Mining and Predicting Development Activities

by Thomas Zimmermann

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