Associating Natural Language Comment and Source Code Entities

Comments are an integral part of software development; they are natural language descriptions associated with source code elements. Understanding explicit associations can be useful in improving code comprehensibility and maintaining the consistency between code and comments. As an initial step towards this larger goal, we address the task of associating entities in Javadoc comments with elements in Java source code. We propose an approach for automatically extracting supervised data using revision histories of open source projects and present a manually annotated evaluation dataset for this task. We develop a binary classifier and a sequence labeling model by crafting a rich feature set which encompasses various aspects of code, comments, and the relationships between them. Experiments show that our systems outperform several baselines learning from the proposed supervision.

Frequent Subgraph Mining Based Collaboration Pattern Analysis for Wikipedia

Online knowledge collaborations, where distributed members without hierarchies self-organize themselvesto create valuable contents, are prevalent in many open production systems such as Wikipedia, GitHub andsocial networks. While many existing studies from network science have been brought to analyse the general interactivebehavioural patterns embedded in these systems, how the collaborations influence the achievement outcomes hasnot been thoroughly investigated. In this paper, we mine the collaboration patterns from a micro perspective to deeplyunderstand the relationships between the collaboration among participants and the qualities of theWikipedia articles.In particular, the subgraphs contained in the collaboration networks derived from theWikipedia revision histories aretaken as the fundamental units to analyse the collaboration diversities from the subgraph properties such as size andtopology. In contrast to the predefined static motifs adopted by the previous works, the collaboration subgraphs aredirectly found from Wikipedia dataset by a frequent subgraph mining algorithm GRAMI, which is able to capturethe real dynamic collaboration patterns. Moreover, the relationships between the co-authors in the subgraphs are alsodiscriminated to further explore the collaboration patterns. The experiments exhibit the statistical properties of thecollaboration subgraphs and the efficiency of them as the metrics for the article quality assessments. We concludethat a small group of editors with relative frequent fixed collaboration patterns contribute more to the excellent articlequality than the professional extents of arbitrary individuals in the collaboration group. This discovery confirms thecommonly insight about collaboration that many heads are always better than one and concretely suggests a potentialexplanation for the increasing prevalence and success of the online knowledge collaborations

Collaborative writing with wikis: an empirical investigation

Purpose – The purpose of this paper is to investigate the process and interaction among group members using wikis to produce collaborative writing (CW) projects, and to compare their collaborative behavior among students at different levels of education. Design/methodology/approach – The study investigated the participation and collaboration of Hong Kong primary school, secondary school, and university students in the process of developing their wiki-based CW projects. Both qualitative and quantitative data were obtained from analyzing the revision histories and the content of wiki pages. Findings – Results indicated that the level of education significantly affected student CW actions, and their interaction and coordination behavior to co-construct the work. Also, the frequency of collaborative activities varied noticeably among the primary, secondary, and university students. Practical implications – The study enriches our understanding of the complex and dynamic process of CW using wikis. It has practical implications on why and how the pedagogy and technology should be implemented differentially for the students at three different levels of education to facilitate collaborative knowledge construction. Originality/value – Research to date is still lacking an in-depth knowledge about the processes and activities involved when students write collaboratively on wikis. Also, no study has yet compared the collaborative behavior among students at different levels of education. The results of this study contribute to the development of new and appropriate modes of group-based collaborative learning at all levels of the education system for the twenty-first century.

Inconsistency Detection in Multilingual Knowledge Sharing

Multilingual knowledge sharing imposes new requirements on knowledge management systems so as to present digital knowledge resources in multiple languages. Knowledge sharing is degraded by inconsistencies such as contents omitted or altered in one of the languages. To resolve this issue, we present a mechanism for detecting inconsistencies in multilingual knowledge sharing. A state transition model is proposed to define the states of the multilingual contents, the set of actions, and the set of transition functions. Inconsistency detection rules are designed to represent the states of the multilingual contents and thus permit the identification of inconsistencies in knowledge sharing. The analysis of a multilingual Wikipedia article indicates that inconsistencies are present in multilingual contents generated by collaboration. In experiments, the proposed mechanism is applied to a test set of revision histories of multilingual articles; the outcome shows satisfactory results with an average precision of 88% in detecting inconsistencies and a recall of 86%. While the proposal considers only user edit actions, it can detect inconsistencies which will be useful in allowing Natural Language Processing (NLP) based systems to synchronize multilingual contents in an early phase.

On the Social Nature of Linguistic Prescriptions

The paper proposes an empirical method to investigate linguistic prescriptions as inherent corrective behaviors. The behaviors in question may but need not necessarily be supported by any explicit knowledge of rules. It is possible to gain insight into them, for example by extracting information about corrections from revision histories of texts (or by analyzing speech corpora where users correct themselves or one another). One easily available source of such information is the revision history of Wikipedia. As is shown, the most frequent and short corrections are limited to linguistic errors such as typos (and editorial conventions adopted in Wikipedia). By perusing an automatically generated revision corpus, one gains insight into the prescriptive nature of language empirically. At the same time, the prescriptions offered are not reducible to descriptions of the most frequent linguistic use.