Towards large-scale information integration

Anderson, Kenneth M.; Sherba, Susanne A.; Lepthien, William Van

doi:10.1145/581400.581403

Cited by 10 publications

(4 citation statements)

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“…Complexity. Technical issues such as the sheer number of artefacts to be traced and the complex relationship between artefacts hamper traceability [18,25,26]. Non-functional requirements tracing is particularly complex.…”

Section: Technical Issuesmentioning

confidence: 99%

The Barriers to Traceability and their Potential Solutions: Towards a Reference Framework

Regan

McCaffery

McDaid

et al. 2012

2012 38th Euromicro Conference on Software Engineering and Advanced Applications

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Abstract-Traceabilityof software artifacts, from requirements to design and through implementation and quality assurance, has long been promoted by the research and expert practitioner communities. However, evidence indicates that, with the exception of those operating in the safety critical domain, few software companies choose to implement traceability processes, often due to associated cost and complexity issues. This paper presents a review of traceability literature including the implementation of traceability in real organizations. Through both analyzing case studies and research published by leading traceability researchers, this paper synthesizes the barriers faced by organizations while implementing traceability, along with proposed solutions to the barriers. Additionally, given the importance of traceability in the regulated domain of safety critical software, the paper compares the barriers for organizations operating inside and outside of this domain.

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Section: Technical Issuesmentioning

confidence: 99%

The Barriers to Traceability and their Potential Solutions: Towards a Reference Framework

Regan

McCaffery

McDaid

et al. 2012

2012 38th Euromicro Conference on Software Engineering and Advanced Applications

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show abstract

“…This technique is limited to tracing between structured or semi-structured artifacts. Translation techniques meanwhile translate heterogeneous artifacts into a homogeneous format (Anderson et al, 2002). Traceability links can then be automatically generated.…”

Section: Rationale and Related Workmentioning

confidence: 99%

Automated Techniques for Capturing Custom Traceability Links Across Heterogeneous Artifacts

Asuncion

Taylor²

2011

Software and Systems Traceability

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“…The goal of software traceability in software engineering is to discover and manage the relationships that exist between these software artifacts to facilitate the efficient retrieval of relevant information, which is necessary for many software engineering tasks [2,3]. There is a correspondence between this domain and HTN planning; each artifact can be viewed as a primitive/non-primitive task, and relationships between artifacts can be viewed as HTN structures.…”

Section: Introductionmentioning

confidence: 99%

Learning hierarchical task network domains from partially observed plan traces

Zhuo

Muñoz-Ávila

Yang

2014

Artificial Intelligence

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Hierarchical Task Network (HTN) planning is an effective yet knowledge intensive problem-solving technique. It requires humans to encode knowledge in the form of methods and action models. Methods describe how to decompose tasks into subtasks and the preconditions under which those methods are applicable whereas action models describe how actions change the world. Encoding such knowledge is a difficult and time-consuming process, even for domain experts. In this paper, we propose a new learning algorithm, called HTNLearn, to help acquire HTN methods and action models. HTNLearn receives as input a collection of plan traces with partially annotated intermediate state information, and a set of annotated tasks that specify the conditions before and after the tasks' completion. In addition, plan traces are annotated with potentially empty partial decomposition trees that record the processes of decomposing tasks to subtasks. HTNLearn outputs are a collection of methods and action models. HTNLearn first encodes constraints about the methods and action models as a constraint satisfaction problem, and then solves the problem using a weighted MAX-SAT solver. HTNLearn can learn methods and action models simultaneously from partially observed plan traces (i.e., plan traces where the intermediate states are partially observable). We test HTNLearn in several HTN domains. The experimental results show that our algorithm HTNLearn is both effective and efficient.

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Towards large-scale information integration

Cited by 10 publications

References 0 publications

The Barriers to Traceability and their Potential Solutions: Towards a Reference Framework

The Barriers to Traceability and their Potential Solutions: Towards a Reference Framework

Automated Techniques for Capturing Custom Traceability Links Across Heterogeneous Artifacts

Learning hierarchical task network domains from partially observed plan traces

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