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Dive into the research topics where Jane Cleland-Huang is active.

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Featured researches published by Jane Cleland-Huang.


IEEE Transactions on Software Engineering | 2003

Event-based traceability for managing evolutionary change

Jane Cleland-Huang; Carl K. Chang; Mark J. Christensen

Although the benefits of requirements traceability are widely recognized, the actual practice of maintaining a traceability scheme is not always entirely successful. The traceability infrastructure underlying a software system tends to erode over its lifetime, as time-pressured practitioners fail to consistently maintain links and update impacted artifacts each time a change occurs, even with the support of automated systems. This paper proposes a new method of traceability based upon event-notification and is applicable even in a heterogeneous and globally distributed development environment. Traceable artifacts are no longer tightly coupled but are linked through an event service, which creates an environment in which change is handled more efficiently, and artifacts and their related links are maintained in a restorable state. The method also supports enhanced project management for the process of updating and maintaining the system artifacts.


international conference on software engineering | 2005

Goal-centric traceability for managing non-functional requirements

Jane Cleland-Huang; Raffaella Settimi; Oussama BenKhadra; Eugenia Berezhanskaya; Selvia Christina

This paper describes a goal centric approach for effectively maintaining critical system qualities such as security, performance, and usability throughout the lifetime of a software system. In goal centric traceability (GCT) non-functional requirements and their interdependencies are modeled as softgoals in a softgoal interdependency graph (SIG). A probabilistic network model is then used to dynamically retrieve links between classes affected by a functional change and elements within the SIG. These links enable developers to identify potentially impacted goals; to analyze the level of impact on those goals; to make informed decisions concerning the implementation of the proposed change; and finally to develop appropriate risk mitigating strategies. This paper also reports experimental results for the link retrieval and illustrates the GCT process through an example of a change applied to a road management system.


international conference on requirements engineering | 2005

Utilizing supporting evidence to improve dynamic requirements traceability

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.


IEEE Computer | 2007

Best Practices for Automated Traceability

Jane Cleland-Huang; Raffaella Settimi; Eli Romanova; Brian Berenbach; Stephen Clark

Automated traceability applies information-retrieval techniques to generate candidate links, sharply reducing the effort of manual approaches to build and maintain a requirements trace matrix as well as providing after-the-fact traceability in legacy documents.The authors describe nine best practices for implementing effective automated traceability.


international conference on software engineering | 2010

A machine learning approach for tracing regulatory codes to product specific requirements

Jane Cleland-Huang; Adam Czauderna; Marek Gibiec; John Emenecker

Regulatory standards, designed to protect the safety, security, and privacy of the public, govern numerous areas of software intensive systems. Project personnel must therefore demonstrate that an as-built system meets all relevant regulatory codes. Current methods for demonstrating compliance rely either on after-the-fact audits, which can lead to significant refactoring when regulations are not met, or else require analysts to construct and use traceability matrices to demonstrate compliance. Manual tracing can be prohibitively time-consuming; however automated trace retrieval methods are not very effective due to the vocabulary mismatches that often occur between regulatory codes and product level requirements. This paper introduces and evaluates two machine-learning methods, designed to improve the quality of traces generated between regulatory codes and product level requirements. The first approach uses manually created traceability matrices to train a trace classifier, while the second approach uses web-mining techniques to reconstruct the original trace query. The techniques were evaluated against security regulations from the USA governments Health Insurance Privacy and Portability Act (HIPAA) traced against ten healthcare related requirements specifications. Results demonstrated improvements for the subset of HIPAA regulations that exhibited high fan-out behavior across the requirements datasets.


ieee international conference on requirements engineering | 2006

The Detection and Classification of Non-Functional Requirements with Application to Early Aspects

Jane Cleland-Huang; Raffaella Settimi; Xuchang Zou; Peter Solc

This paper introduces an information retrieval based approach for automating the detection and classification of non-functional requirements (NFRs). Early detection of NFRs is useful because it enables system level constraints to be considered and incorporated into early architectural designs as opposed to being refactored in at a later time. Candidate NFRs can be detected in both structured and unstructured documents, including requirements specifications that contain scattered and non-categorized NFRs, and freeform documents such as meeting minutes, interview notes, and memos containing stakeholder comments documenting their NFR related needs. This paper describes the classification algorithm and then evaluates its effectiveness in an experiment based on fifteen requirements specifications developed as term projects by MS students at DePaul University. An additional case study is also described in which the approach is used to classifying NFRs from a large free form requirements document obtained from Siemens Logistics and Automotive Organization


Requirements Engineering | 2007

Automated classification of non-functional requirements

Jane Cleland-Huang; Raffaella Settimi; Xuchang Zou; Peter Solc

This paper describes a technique for automating the detection and classification of non-functional requirements related to properties such as security, performance, and usability. Early detection of non-functional requirements enables them to be incorporated into the initial architectural design instead of being refactored in at a later date. The approach is used to detect and classify stakeholders’ quality concerns across requirements specifications containing scattered and non-categorized requirements, and also across freeform documents such as meeting minutes, interview notes, and memos. This paper first describes the classification algorithm and then evaluates its effectiveness through reporting a series of experiments based on 30 requirements specifications developed as term projects by MS students at DePaul University. A new and iterative approach is then introduced for training or retraining a classifier to detect and classify non-functional requirements (NFR) in datasets dissimilar to the initial training sets. This approach is evaluated against a large free-form requirements document obtained from Siemens Logistics and Automotive Organization. Although to the NFR classifier is unable to detect all of the NFRs, it is useful for supporting an analyst in the error-prone task of manually discovering NFRs, and furthermore can be used to quickly analyse large and complex documents in order to search for NFRs.


international conference on software engineering | 2014

Software traceability: trends and future directions

Jane Cleland-Huang; Orlena Gotel; Jane Huffman Hayes; Patrick Mäder; Andrea Zisman

Software traceability is a sought-after, yet often elusive quality in software-intensive systems. Required in safety-critical systems by many certifying bodies, such as the USA Federal Aviation Authority, software traceability is an essential element of the software development process. In practice, traceability is often conducted in an ad-hoc, after-the-fact manner and, therefore, its benefits are not always fully realized. Over the past decade, researchers have focused on specific areas of the traceability problem, developing more sophisticated tooling, promoting strategic planning, applying information retrieval techniques capable of semi-automating the trace creation and maintenance process, developing new trace query languages and visualization techniques that use trace links, and applying traceability in specific domains such as Model Driven Development, product line systems, and agile project environments. In this paper, we build upon a prior body of work to highlight the state-of-the-art in software traceability, and to present compelling areas of research that need to be addressed.


international conference on software engineering | 2011

On-demand feature recommendations derived from mining public product descriptions

Horatiu Dumitru; Marek Gibiec; Negar Hariri; Jane Cleland-Huang; Bamshad Mobasher; Carlos Castro-Herrera; Mehdi Mirakhorli

We present a recommender system that models and recommends product features for a given domain. Our approach mines product descriptions from publicly available online specifications, utilizes text mining and a novel incremental diffusive clustering algorithm to discover domain-specific features, generates a probabilistic feature model that represents commonalities, variants, and cross-category features, and then uses association rule mining and the k-Nearest-Neighbor machine learning strategy to generate product specific feature recommendations. Our recommender system supports the relatively labor-intensive task of domain analysis, potentially increasing opportunities for re-use, reducing time-to-market, and delivering more competitive software products. The approach is empirically validated against 20 different product categories using thousands of product descriptions mined from a repository of free software applications.


Archive | 2012

Software and Systems Traceability

Jane Cleland-Huang; Orlena Gotel; Andrea Zisman

Software and Systems Traceability provides a comprehensive description of the practices and theories of software traceability across all phases of the software development lifecycle. The term software traceabilityis derivedfrom the concept of requirements traceability. Requirements traceability is the ability to track a requirement all the way from its origins to the downstream work products that implement that requirement in a software system. Software traceability is defined as the ability to relate the various types of software artefacts created during the development of software systems. Traceability relations can improve the quality of a product being developed, and reduce the time and cost of development. More specifically, traceability relations can support evolution of software systems, reuse of parts of a system by comparing components of new and existing systems, validation that a system meets its requirements, understanding of the rationale for certain design and implementation decisions, and analysis of the implications of changes in the system.

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Mehdi Mirakhorli

Rochester Institute of Technology

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Patrick Mäder

Technische Universität Ilmenau

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Michael Vierhauser

Johannes Kepler University of Linz

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