Mining version histories to guide software changes

By Thomas Zimmermann, Peter Weißgerber, Stephan Diehl, Andreas Zeller. Proceedings. 26th International Conference on Software Engineering, 2004

1,001 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/d61f6562c34b482dc195ccbd737500718d9be249

How researchers see this paper

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

In Information Systems

1. Premkumar T. Devanbu
2. Thomas Zimmermann
3. Miltiadis Allamanis
4. Ahmed E. Hassan
5. Andreas Zeller
6. Tien Nhut Nguyen
7. Charles Sutton
8. Sunghun Kim

In Software

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

Citations in the corpus

• Neural Networks for Modeling Source Code Edits (2018, background): “Zimmermann et al. (2005) instead predicts the location of the next change, given the location of the previous change.”
• The promises and perils of mining git (2009, neutral): “A. E. Housman, A Shropshire Lad”
• Hipikat: a project memory for software development (2005, background): “Zimmerman et al. [23] and Ying et al. [24] applied data mining techniques to source repositories to find change patterns, which can be used to make recommendations about potentially relevant files to a developer working on a software change.”
• PR-Miner: automatically extracting implicit programming rules and detecting violations in large software code (2005, methodology): “Besides PR-Miner and our previous work, CP-Miner [18], mining techniques can be used in various aspects of software engineering, including development management [21], software evolution [29, 31], system understanding[24], fault-prone file identification [22] and software reuse [30], just to name a ”
• The road ahead for Mining Software Repositories (2008, background): “HATARI [60], Hipikat [12], Myln [35], and eROSE [68] are examples of MSR research along this direction.”
• Is it a bug or an enhancement?: a text-based approach to classify change requests (2008, background): “[25] proposed a change-coupling identification approach capable of recovering fine-grain co-changing entities (e.”
• 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].”
• Automatic bug triage using text categorization (2004, methodology): “mined source version histories to determine association rules which can then be used to predict files (or smaller program elements, such as functions and variables) that usually change together [11].”
• Sustainability Design and Software: The Karlskrona Manifesto (2015, background): “More recent relevant publications for example include [11], [12], [13].”
• Predicting source code changes by mining change history (2004, neutral): “Index Terms—Enhancement, maintainability, clustering, classification, association rules, data mining.”
• Classifying Software Changes: Clean or Buggy? (2008, neutral): “In other research, cumulative bug and change counts are commonly used as bug predictors [11], [33], [38], [40], [47], [48], [56].”
• An empirical study on the evolution of design patterns (2007, methodology): “We adopt a time-windowing approach that considers a MT as sequence of file revisions that share the same author, branch, and commit notes, and such that the difference between the timestamps of two subsequent commits is less or equal than 200 seconds [27].”
• Mining metrics to predict component failures (2006, background): “In recent years, researchers have learned to exploit the vast amount of data that is contained in software repositories such as version and bug databases [16, 17, 19, 22].”
• 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].”
• The influence of organizational structure on software quality (2008, background): “Zimmermann et al. [37] mined source code repositories of eight large scale open source systems (IBM Eclipse, Postgres, KOffice, gcc, Gimp, JBoss, JEdit and Python) to predict where future changes will take place in these systems.”
• 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]”