Predicting source code changes by mining change history

By Annie T. T. Ying, Gail C. Murphy, Raymond T. Ng, Mark Chu-Carroll. IEEE Transactions on Software Engineering, 2004

575 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/f8ac100d0957dd50774c81ce1853555688d3c41b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Andreas Zeller
2. Thomas Zimmermann
3. Tien Nhut Nguyen
4. Massimiliano Di Penta
5. Baishakhi Ray
6. Daniel Tarlow
7. David Bieber
8. Kevin Swersky

In Information Systems

1. Miltiadis Allamanis
2. Tien Nhut Nguyen
3. Premkumar T. Devanbu
4. Baishakhi Ray
5. Thomas Zimmermann
6. Charles Sutton
7. Andreas Zeller
8. Massimiliano Di Penta

In Software

1. Tien Nhut Nguyen
2. Baishakhi Ray
3. Miltiadis Allamanis
4. Thomas Zimmermann
5. Benjamin Livshits
6. Massimiliano Di Penta
7. Yangruibo Ding
8. Premkumar T. Devanbu

Citations in the corpus

• Mining version histories to guide software changes (2005, background): “Furthermore, coupling that can be found via program analysis [33] need not be repeated in ROSE’s suggestions.”
• The road ahead for Mining Software Repositories (2008, background): “[66], Shirabad [59], Hassan and Holt [25] show that suggestions based on historical co-changes are on average accurate 30% of the time (i.”
• DynaMine: finding common error patterns by mining software revision histories (2005, methodology): “Recent research improves on co-change by applying data mining techniques to revision histories [37, 40].”
• Change Distilling:Tree Differencing for Fine-Grained Source Code Change Extraction (2007, background): “For instance, Ying et al. or Zimmermann et al. developed approaches that guide programmers along related changes by telling them “programmers who changed these functions also changed . . . ” [45], [47].”
• Graph-Based Mining of In-the-Wild, Fine-Grained, Semantic Code Change Patterns (2019, background): “Other researchers detect the patterns of changes for program auto-repairing [19], [15] or software evolution [45], [48], [40].”
• Neural Networks for Modeling Source Code Edits (2018, background): “ld suffer from the same issues. Software Engineering and Document Editing. Some work in software engineering has modeled edits to code, but it operates at a coarser level of granularity. For example, Ying et al. (2004); Zimmermann et al. (2005); Hu et al. (2010) look at development histories and ide”
• Mining version histories to guide software changes (2004, background): “Furthermore, coupling that can be found via program analysis [33] need not be repeated in ROSE’s suggestions.”
• Hierarchical Clustering for Software Architecture Recovery (2007, background): “…have been applied to a variety of problems besides architecture recovery, for example, identifying objects, inferring configuration structures, discovering reuse patterns, and understanding Web-based applications [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36].”
• Is it a bug or an enhancement?: a text-based approach to classify change requests (2008, methodology): “Ying [24] presented an approach for the identification of logical coupling that computes association rules among files by applying data mining techniques on CVS repositories.”
• CODIT: Code Editing With Tree-Based Neural Models (2018, background): “, adding, deleting, or replacing code elements) through applying such frequent change patterns [19], [23], [24], [25]”