Changes and Bugs: Mining and Predicting Development Activities
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The document discusses mining software archives and bug databases to predict software development activities and defects. It presents two main contributions: 1) fine-grained analysis of version archives to identify usage patterns and cross-cutting changes, and 2) mining bug databases to predict defects based on dependencies and the increased likelihood of defects when depending on defect-prone code.
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Software Analytics = Sharing Information
Software Analytics = Sharing InformationThomas Zimmermann This document summarizes a presentation on software analytics. It discusses definitions of software analytics, types of analytics like web and game analytics, the history and various names of software analytics. It outlines key information needs for software development analytics like summarization, alerts, trends and benchmarks. Smart analytics is described as being actionable, real-time, diverse, and sharing insights, methods, models and data. An example is provided of sharing a defect prediction model and discussing challenges with cross-project prediction. Finally, an example is given of sharing insights around skill in the game Halo Reach.
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MSR 2013 PreviewThomas Zimmermann The document outlines keynotes and sessions from a software development conference, highlighting talks by experts such as Gail C. Murphy and Brian Doll, focusing on software productivity, collaboration, and mining techniques. It features a technical track with 36 presentations addressing contemporary issues and trends in software analytics, mobile apps, and bug prediction. Additionally, it describes a mining challenge related to Stack Overflow data aimed at deriving insights into user behavior and software development practices.
Predicting Method Crashes with Bytecode Operations
Predicting Method Crashes with Bytecode OperationsThomas Zimmermann 1) The document presents a technique called ReCrash+ that combines crash prediction using bytecode features with capture and replay to reproduce crashes with lower overhead.
2) It evaluates crash prediction both within and across projects, finding that bytecode features can accurately identify crashed methods.
3) An experiment applying ReCrash+ to a project called SVNKit was able to reproduce 3 crashes by only monitoring the small subset of methods predicted as crash-prone.
Analytics for smarter software development
Analytics for smarter software development Thomas Zimmermann The document discusses how software analytics can empower software development teams to make better decisions by gaining insight from data. It describes how analytics involves using analysis, data, and systematic reasoning to make decisions. The document argues that software analytics can help teams share insights from their data and empower them to gain a better understanding of their software development process.
Characterizing and Predicting Which Bugs Get Reopened
Characterizing and Predicting Which Bugs Get ReopenedThomas Zimmermann This document summarizes a study characterizing and predicting which software bugs get reopened. Through a qualitative survey, researchers identified six main causes of bug reopens: bugs being difficult to reproduce, misunderstood root causes, insufficient bug information, increased bug priority later on, regression bugs, and process-related issues. A quantitative analysis using logistic regression found that bugs found through code analysis tools or human review were less likely to reopen, while bugs found through system or customer testing were more likely to reopen. The analysis also found that bugs opened by people with higher reputations based on past bugs were less likely to reopen.
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Data driven games user researchThomas Zimmermann This document discusses Microsoft's use of data-driven user research methods for game development. It summarizes different types of data collected, including in-game telemetry, Xbox Live usage data, and online forum discussions. Examples are provided of analyses conducted on data from games like Project Gotham Racing 4 and Halo 3 to understand player behavior and identify underutilized game elements. The goal is to better understand the player experience and Xbox ecosystem to inform game design and personalization.
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Empirical Software Engineering at Microsoft ResearchThomas Zimmermann The document discusses empirical software engineering research conducted at Microsoft, focusing on socio-technical congruence, contribution behavior, and the impact of organizational structure on software quality. It covers various studies related to bug reporting, defect prediction, and the influence of distributed development on code quality, as well as recommendations for improving bug triaging processes and enhancing the quality of bug reports. The research highlights the importance of factors such as collaboration, organizational metrics, and the reputation of bug reporters in determining the likelihood of bugs being resolved successfully.
Security trend analysis with CVE topic models
Security trend analysis with CVE topic modelsThomas Zimmermann This document discusses using topic models to analyze trends in vulnerability data from Common Vulnerabilities and Exposures (CVE). Latent Dirichlet allocation is used to identify topics in CVE summaries from 2000-2009. Key trends identified include a decline in buffer overflows and format strings but an increase in SQL injection, cross-site scripting, and application servers. The topics identified generally align well with the Common Weakness Enumeration classifications.
Analytics for software development
Analytics for software developmentThomas Zimmermann 1) The document discusses software analytics and how it can be used to understand what happened in the past, what is currently happening, and what may happen in the future for software development projects.
2) It notes that different stakeholders like researchers, developers, testers, and managers have different needs for analytics and a single tool may not be enough to meet all needs.
3) The presentation outlines challenges with software analytics including data collection, data quality, privacy, understanding different user needs, and user experience. It stresses the importance of making data actionable and accessible for stakeholders.
Characterizing and predicting which bugs get fixed
Characterizing and predicting which bugs get fixedThomas Zimmermann This document discusses factors that affect whether software bugs get fixed. It analyzes bug report data from Microsoft and surveys Microsoft employees. The analysis found that bugs with one reassignment are more likely to be fixed, but too many reassignments decrease likelihood. Bugs opened by people with high reputations and handled within single teams and locations are more likely to be fixed. The survey revealed social factors like seniority, reputation, relationships, and trust also impact the bug fixing process.
Cross-project defect prediction
Cross-project defect predictionThomas Zimmermann This document examines cross-project defect prediction, where a model trained on one project is used to predict defects in another project. In an experiment of 622 cross-project combinations across 12 systems, only 3.4% had successful predictions. However, certain project similarities like domain and characteristics like code reviews increased prediction precision and recall. Decision trees were created to help select project combinations where cross-project prediction is more likely to succeed based on characteristics like intended audience and pre-release bug metrics.
Quality of Bug Reports in Open Source
Quality of Bug Reports in Open SourceThomas Zimmermann The document discusses research into what makes a good bug report based on a survey of over 150 developers. It finds that the most helpful items for fixing bugs are steps to reproduce, stack traces, and observed behavior. The biggest problems causing delays are incomplete information, wrong steps to reproduce, and wrong expected behavior. The research also measured bug report quality and found stack traces and readability correlated with shorter fix times.
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Predicting Subsystem Defects using Dependency Graph Complexities
Predicting Subsystem Defects using Dependency Graph Complexities Thomas Zimmermann The document presents a method for predicting subsystem failures in software projects using dependency graph complexities. It finds that code dependency cycles and large cliques (fully connected subgraphs) correlate with more defects. It also finds that measures of complexity in a subsystem's dependency graph, such as the number of nodes and edges, can be used to predict the number of defects in the subsystem. Evaluating the method on a large Microsoft software project showed it could effectively rank subsystems by likelihood of failure.
Got Myth? Myths in Software Engineering
Got Myth? Myths in Software EngineeringThomas Zimmermann The document summarizes a working session on software engineering myths held on October 4, 2007 in Paris. The session included discussions of common myths in areas such as complexity, bugs, clones, aspect-oriented programming, and project management. Participants shared and debated myths in small group discussions. Some examples of myths discussed include the ideas that complexity leads to more bugs, clones are always bad, and aspect-oriented programs are inherently easier to maintain. The session aimed to evaluate whether such perceptions are truly myths or if aspects of them have been found to hold true in various studies.
Mining Workspace Updates in CVS
Mining Workspace Updates in CVSThomas Zimmermann The document discusses updates and commits made in CVS version control software. It includes logs of revisions for an IBuffer.java file with information on dates, authors, and changes made. It also contains charts showing data on how often updates integrate changes, how many commits lead to integrations, what percentage of integrations have conflicts, and how many conflicting changes are discarded for different projects like JEdit and Python. The conclusion is that CVS can integrate many changes but not all, and developers often have their own workspaces.
Mining Software Archives to Support Software Development
Mining Software Archives to Support Software DevelopmentThomas Zimmermann 1. The document discusses mining software archives and repositories to help guide software developers and predict defects.
2. It describes the eROSE tool which mines past associations between code changes to suggest related files and locations to developers.
3. The BugCache model predicts future defects based on the hypothesis that defects are temporally local, with a cache that loads elements likely to have defects.
Unit testing with JUnit
Unit testing with JUnitThomas Zimmermann Unit testing with JUnit focuses on testing individual units of source code using JUnit. Key aspects covered include:
- Unit testing tests the smallest testable parts of code, while integration testing tests groups of code modules and system testing tests complete integrated systems.
- Tests in JUnit are implemented as public void testX() methods that call methods under test and assert expected outcomes.
- A test suite assembles individual test methods using reflection and executes the suite to automatically run all tests.
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- 1. CHANGES and BUGS Mining and Predicting Software Development Activities Thomas Zimmermann
- 2. Software development Build
- 3. Collaboration Comm. Version Bug Archive Archive Database Mining Software Archives
- 4. 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
- 5. 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).
- 6. 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); }
- 7. 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); }
- 8. 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
- 9. 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
- 10. 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).
- 12. Quality assurance is limited... ...by time... ...and by money.
- 13. Spent resources on the components that need it most, i.e., are most likely to fail.
- 14. 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.
- 16. 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)
- 17. DATA. .
- 18. Data collection six months Release point for to collect Windows Server 2003 defects Dependencies Network Measures Complexity Metrics Defects
- 19. 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)
- 20. 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
- 21. 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
- 22. RESULTS. .
- 23. Prediction Input metrics and measures Model Prediction PCA Regression Metrics Classification SNA Metrics+SNA Ranking
- 24. Classification Has a binary a defect or not? or
- 25. Ranking Which binaries have the most defects? or or ... or
- 28. Classification (logistic regression) SNA increases the recall by 0.10 (at p=0.01) while precision remains comparable.
- 29. Ranking (linear regression) SNA+METRICS increases the correlation by 0.10 (significant at p=0.01)
- 30. 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
- 31. "Piled Higher and Deeper" by Jorge Cham. www.phdcomics.com
- 32. "Piled Higher and Deeper" by Jorge Cham. www.phdcomics.com
- 33. "Piled Higher and Deeper" by Jorge Cham. www.phdcomics.com
- 34. 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).