Chuan Duan
DePaul University
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Featured researches published by Chuan Duan.
international conference on requirements engineering | 2005
Jane Cleland-Huang; Raffaella Settimi; Chuan Duan; Xuchang Zou
Requirements traceability provides critical support throughout all phases of a software development project. However practice has repeatedly shown the difficulties involved in long term maintenance of traditional traceability matrices. Dynamic retrieval methods minimize the need for creating and maintaining explicit links and can significantly reduce the effort required to perform a manual trace. Unfortunately they suffer from recall and precision problems. This paper introduces three strategies for incorporating supporting information into a probabilistic retrieval algorithm in order to improve the performance of dynamic requirements traceability. The strategies include hierarchical modeling, logical clustering of artifacts, and semi-automated pruning of the probabilistic network. Experimental results indicate that enhancement strategies can be used effectively to improve trace retrieval results thereby increasing the practicality of utilizing dynamic trace retrieval methods.
automated software engineering | 2007
Chuan Duan; Jane Cleland-Huang
Automated trace tools dynamically generate links between various software artifacts such as requirements, design elements, code, test cases, and other less structured supplemental documents. Trace algorithms typically utilize information retrieval methods to compute similarity scores between pairs of artifacts. Results are returned to the user as a ranked set of candidate links, and the user is then required to evaluate the results through performing a top-down search through the list. Although clustering methods have previously been shown to improve the performance of information retrieval algorithms by increasing understandability of the results and minimizing human analysis effort, their usefulness in automated traceability tools has not yet been explored. This paper evaluates and compares the effectiveness of several existing clustering methods to support traceability; describes a technique for incorporating them into the automated traceability process; and proposes new techniques based on the concepts of theme cohesion and coupling to dynamically identify optimal clustering granularity and to detect cross-cutting concerns that would otherwise remain undetected by standard clustering algorithms. The benefits of utilizing clustering in automated trace retrieval are then evaluated through a case study
ieee international conference on requirements engineering | 2006
Jun Lin; Chan Chou Lin; J.C. Huang; Raffaella Settimi; J. Amaya; G. Bedford; Brian Berenbach; O.B. Khadra; Chuan Duan; Xuchang Zou
Poirot is a Web-based tool supporting traceability of distributed heterogeneous software artifacts. A probabilistic network model is used to generate traces between requirements, design elements, code and other artifacts stored in distributed 3rd party case tools such as DOORS, rational rose, and source code repositories. The tool is designed with extensibility in mind, so that additional artifact types and 3rd party case tools can be easily added. Trace results are displayed in both a textual and visual format. This paper briefly describes the underlying probabilistic model, and the user interface of the tool, and then discusses Poirots deployment and use in an industrial setting
ieee international conference on requirements engineering | 2007
Paula Laurent; Jane Cleland-Huang; Chuan Duan
Budgetary restrictions and time-to-market deadlines often require stakeholders to prioritize requirements and decide which ones to include in a given product release. Lack of an effective prioritization and triage process can lead to problems such as missed deadlines, disorganized development efforts, and late discovery of architecturally significant requirements. Existing prioritization techniques do not provide sufficient automation for large projects with hundreds of stakeholders and thousands of potentially conflicting requests and requirements. This paper therefore proposes an approach for automating a significant part of the prioritization process. The proposed method utilizes a probabilistic traceability model combined with a standard hierarchical clustering algorithm to cluster incoming stakeholder requests into hierarchical feature sets. Additional cross-cutting clusters are then generated to represent factors such as architecturally significant requirements or impacted business goals. Prioritization decisions are initially made at the feature level and then more critical requirements are promoted according to their relationships with the identified cross-cutting concerns. The approach is illustrated and evaluated through a case study applied to the requirements of the ice breaker system.
parallel computing | 2009
Jane Cleland-Huang; Horatiu Dumitru; Chuan Duan; Carlos Castro-Herrera
The result is stable, focused, dynamic discussion threads that avoid redundant ideas and engage thousands of stakeholders.
Requirements Engineering | 2009
Chuan Duan; Paula Laurent; Jane Cleland-Huang; Charles Kwiatkowski
Time-to-market deadlines and budgetary restrictions require stakeholders to carefully prioritize requirements and determine which ones to implement in a given product release. Unfortunately, existing prioritization techniques do not provide sufficient automation for large projects with hundreds of stakeholders and thousands of potentially conflicting requests and requirements. This paper therefore describes a new approach for automating a significant part of the prioritization process. The proposed method utilizes data-mining and machine learning techniques to prioritize requirements according to stakeholders’ interests, business goals, and cross-cutting concerns such as security or performance requirements. The effectiveness of the approach is illustrated and evaluated through two case studies based on the requirements of the Ice Breaker System, and also on a set of stakeholders’ raw feature requests mined from the discussion forum of an open source product named SugarCRM.
requirements engineering | 2008
Carlos Castro-Herrera; Chuan Duan; Jane Cleland-Huang; Bamshad Mobasher
Requirements related problems, especially those originating from inadequacies in the human-intensive task of eliciting stakeholderspsila needs and desires, have contributed to many failed and challenged software projects. This is especially true for large and complex projects in which requirements knowledge is distributed across thousands of stakeholders. This short paper introduces a new process and related framework that utilizes data mining and recommender technologies to create an open, scalable, and inclusive requirements elicitation process capable of supporting projects with thousands of stakeholders. The approach is illustrated and evaluated using feature requests mined from an open source software product.
conference on information and knowledge management | 2008
Chuan Duan; Jane Cleland-Huang; Bamshad Mobasher
Managing large-scale software projects involves a number of activities such as viewpoint extraction, feature detection, and requirements management, all of which require a human analyst to perform the arduous task of organizing requirements into meaningful topics and themes. Automating these tasks through the use of data mining techniques such as clustering could potentially increase both the efficiency of performing the tasks and the reliability of the results. Unfortunately, the unique characteristics of this domain, such as high dimensional, sparse, noisy data sets, resulting from short and ambiguous expressions of need, as well as the need for the interactive engagement of stakeholders at various stages of the process, present difficult challenges for standard clustering algorithms. In this paper, we propose a semi-supervised clustering framework, based on a combination of consensus-based and constrained clustering techniques, which can effectively handle these challenges. Specifically, we provide a probabilistic analysis for informative constraint generation based on a co-association matrix, and utilize consensus clustering to combine multiple constrained partitions in order to generate high-quality, robust clusters. Our approach is validated through a series of experiments on six well-studied TREC data sets and on two sets of user requirements.
requirements engineering: foundation for software quality | 2015
Chuan Duan; Horatiu Dumitru; Jane Cleland-Huang; Bamshad Mobasher
[Context & motivation:] Software development projects involving geographically dispersed stakeholders often use web-based discussion forums to gather feature requests. Our previous study showed that users have a tendency to create redundant threads as well as large unfocused mega-threads. [Question/problem:] In this paper we propose novel solution for integrating user feedback into the process of dynamically and iteratively clustering features into discussion threads. [Principal ideas/results:] We integrate feed back in the form of stick-together and move-apart advice, plus user-defined tags into our consensus based clustering process. [Contribution:] Experimental results demonstrate that our approach is able to deliver high quality and stable clusters to facilitate forum-based requirements elicitation.
acm symposium on applied computing | 2009
Carlos Castro-Herrera; Chuan Duan; Jane Cleland-Huang; Bamshad Mobasher