2,718 research outputs found
TLAD 2010 Proceedings:8th international workshop on teaching, learning and assesment of databases (TLAD)
This is the eighth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2010), which once again is held as a workshop of BNCOD 2010 - the 27th International Information Systems Conference. TLAD 2010 is held on the 28th June at the beautiful Dudhope Castle at the Abertay University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.This year, the workshop includes an invited talk given by Richard Cooper (of the University of Glasgow) who will present a discussion and some results from the Database Disciplinary Commons which was held in the UK over the academic year. Due to the healthy number of high quality submissions this year, the workshop will also present seven peer reviewed papers, and six refereed poster papers. Of the seven presented papers, three will be presented as full papers and four as short papers. These papers and posters cover a number of themes, including: approaches to teaching databases, e.g. group centered and problem based learning; use of novel case studies, e.g. forensics and XML data; techniques and approaches for improving teaching and student learning processes; assessment techniques, e.g. peer review; methods for improving students abilities to develop database queries and develop E-R diagrams; and e-learning platforms for supporting teaching and learning
TLAD 2010 Proceedings:8th international workshop on teaching, learning and assesment of databases (TLAD)
This is the eighth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2010), which once again is held as a workshop of BNCOD 2010 - the 27th International Information Systems Conference. TLAD 2010 is held on the 28th June at the beautiful Dudhope Castle at the Abertay University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.This year, the workshop includes an invited talk given by Richard Cooper (of the University of Glasgow) who will present a discussion and some results from the Database Disciplinary Commons which was held in the UK over the academic year. Due to the healthy number of high quality submissions this year, the workshop will also present seven peer reviewed papers, and six refereed poster papers. Of the seven presented papers, three will be presented as full papers and four as short papers. These papers and posters cover a number of themes, including: approaches to teaching databases, e.g. group centered and problem based learning; use of novel case studies, e.g. forensics and XML data; techniques and approaches for improving teaching and student learning processes; assessment techniques, e.g. peer review; methods for improving students abilities to develop database queries and develop E-R diagrams; and e-learning platforms for supporting teaching and learning
Grand Challenges of Traceability: The Next Ten Years
In 2007, the software and systems traceability community met at the first
Natural Bridge symposium on the Grand Challenges of Traceability to establish
and address research goals for achieving effective, trustworthy, and ubiquitous
traceability. Ten years later, in 2017, the community came together to evaluate
a decade of progress towards achieving these goals. These proceedings document
some of that progress. They include a series of short position papers,
representing current work in the community organized across four process axes
of traceability practice. The sessions covered topics from Trace Strategizing,
Trace Link Creation and Evolution, Trace Link Usage, real-world applications of
Traceability, and Traceability Datasets and benchmarks. Two breakout groups
focused on the importance of creating and sharing traceability datasets within
the research community, and discussed challenges related to the adoption of
tracing techniques in industrial practice. Members of the research community
are engaged in many active, ongoing, and impactful research projects. Our hope
is that ten years from now we will be able to look back at a productive decade
of research and claim that we have achieved the overarching Grand Challenge of
Traceability, which seeks for traceability to be always present, built into the
engineering process, and for it to have "effectively disappeared without a
trace". We hope that others will see the potential that traceability has for
empowering software and systems engineers to develop higher-quality products at
increasing levels of complexity and scale, and that they will join the active
community of Software and Systems traceability researchers as we move forward
into the next decade of research
Grand Challenges of Traceability: The Next Ten Years
In 2007, the software and systems traceability community met at the first
Natural Bridge symposium on the Grand Challenges of Traceability to establish
and address research goals for achieving effective, trustworthy, and ubiquitous
traceability. Ten years later, in 2017, the community came together to evaluate
a decade of progress towards achieving these goals. These proceedings document
some of that progress. They include a series of short position papers,
representing current work in the community organized across four process axes
of traceability practice. The sessions covered topics from Trace Strategizing,
Trace Link Creation and Evolution, Trace Link Usage, real-world applications of
Traceability, and Traceability Datasets and benchmarks. Two breakout groups
focused on the importance of creating and sharing traceability datasets within
the research community, and discussed challenges related to the adoption of
tracing techniques in industrial practice. Members of the research community
are engaged in many active, ongoing, and impactful research projects. Our hope
is that ten years from now we will be able to look back at a productive decade
of research and claim that we have achieved the overarching Grand Challenge of
Traceability, which seeks for traceability to be always present, built into the
engineering process, and for it to have "effectively disappeared without a
trace". We hope that others will see the potential that traceability has for
empowering software and systems engineers to develop higher-quality products at
increasing levels of complexity and scale, and that they will join the active
community of Software and Systems traceability researchers as we move forward
into the next decade of research
The development of a program analysis environment for Ada
A unit level, Ada software module testing system, called Query Utility Environment for Software Testing of Ada (QUEST/Ada), is described. The project calls for the design and development of a prototype system. QUEST/Ada design began with a definition of the overall system structure and a description of component dependencies. The project team was divided into three groups to resolve the preliminary designs of the parser/scanner: the test data generator, and the test coverage analyzer. The Phase 1 report is a working document from which the system documentation will evolve. It provides history, a guide to report sections, a literature review, the definition of the system structure and high level interfaces, descriptions of the prototype scope, the three major components, and the plan for the remainder of the project. The appendices include specifications, statistics, two papers derived from the current research, a preliminary users' manual, and the proposal and work plan for Phase 2
Doctor of Philosophy
dissertationManual annotation of clinical texts is often used as a method of generating reference standards that provide data for training and evaluation of Natural Language Processing (NLP) systems. Manually annotating clinical texts is time consuming, expensive, and requires considerable cognitive effort on the part of human reviewers. Furthermore, reference standards must be generated in ways that produce consistent and reliable data but must also be valid in order to adequately evaluate the performance of those systems. The amount of labeled data necessary varies depending on the level of analysis, the complexity of the clinical use case, and the methods that will be used to develop automated machine systems for information extraction and classification. Evaluating methods that potentially reduce cost, manual human workload, introduce task efficiencies, and reduce the amount of labeled data necessary to train NLP tools for specific clinical use cases are active areas of research inquiry in the clinical NLP domain. This dissertation integrates a mixed methods approach using methodologies from cognitive science and artificial intelligence with manual annotation of clinical texts. Aim 1 of this dissertation identifies factors that affect manual annotation of clinical texts. These factors are further explored by evaluating approaches that may introduce efficiencies into manual review tasks applied to two different NLP development areas - semantic annotation of clinical concepts and identification of information representing Protected Health Information (PHI) as defined by HIPAA. Both experiments integrate iv different priming mechanisms using noninteractive and machine-assisted methods. The main hypothesis for this research is that integrating pre-annotation or other machineassisted methods within manual annotation workflows will improve efficiency of manual annotation tasks without diminishing the quality of generated reference standards
Gorilla: Large Language Model Connected with Massive APIs
Large Language Models (LLMs) have seen an impressive wave of advances
recently, with models now excelling in a variety of tasks, such as mathematical
reasoning and program synthesis. However, their potential to effectively use
tools via API calls remains unfulfilled. This is a challenging task even for
today's state-of-the-art LLMs such as GPT-4, largely due to their inability to
generate accurate input arguments and their tendency to hallucinate the wrong
usage of an API call. We release Gorilla, a finetuned LLaMA-based model that
surpasses the performance of GPT-4 on writing API calls. When combined with a
document retriever, Gorilla demonstrates a strong capability to adapt to
test-time document changes, enabling flexible user updates or version changes.
It also substantially mitigates the issue of hallucination, commonly
encountered when prompting LLMs directly. To evaluate the model's ability, we
introduce APIBench, a comprehensive dataset consisting of HuggingFace,
TorchHub, and TensorHub APIs. The successful integration of the retrieval
system with Gorilla demonstrates the potential for LLMs to use tools more
accurately, keep up with frequently updated documentation, and consequently
increase the reliability and applicability of their outputs. Gorilla's code,
model, data, and demo are available at https://gorilla.cs.berkeley.ed
On-the-Fly Syntax Highlighting using Neural Networks
With the presence of online collaborative tools for software developers,
source code is shared and consulted frequently, from code viewers to merge
requests and code snippets. Typically, code highlighting quality in such
scenarios is sacrificed in favor of system responsiveness. In these on-the-fly
settings, performing a formal grammatical analysis of the source code is not
only expensive, but also intractable for the many times the input is an invalid
derivation of the language. Indeed, current popular highlighters heavily rely
on a system of regular expressions, typically far from the specification of the
language's lexer. Due to their complexity, regular expressions need to be
periodically updated as more feedback is collected from the users and their
design unwelcome the detection of more complex language formations. This paper
delivers a deep learning-based approach suitable for on-the-fly grammatical
code highlighting of correct and incorrect language derivations, such as code
viewers and snippets. It focuses on alleviating the burden on the developers,
who can reuse the language's parsing strategy to produce the desired
highlighting specification. Moreover, this approach is compared to nowadays
online syntax highlighting tools and formal methods in terms of accuracy and
execution time, across different levels of grammatical coverage, for three
mainstream programming languages. The results obtained show how the proposed
approach can consistently achieve near-perfect accuracy in its predictions,
thereby outperforming regular expression-based strategies.Comment: Accepted for publication in the ACM Joint European Software
Engineering Conference and Symposium on the Foundations of Software
Engineering (ESEC/FSE 2022
Mapping the Structure and Evolution of Software Testing Research Over the Past Three Decades
Background: The field of software testing is growing and rapidly-evolving.
Aims: Based on keywords assigned to publications, we seek to identify
predominant research topics and understand how they are connected and have
evolved.
Method: We apply co-word analysis to map the topology of testing research as
a network where author-assigned keywords are connected by edges indicating
co-occurrence in publications. Keywords are clustered based on edge density and
frequency of connection. We examine the most popular keywords, summarize
clusters into high-level research topics, examine how topics connect, and
examine how the field is changing.
Results: Testing research can be divided into 16 high-level topics and 18
subtopics. Creation guidance, automated test generation, evolution and
maintenance, and test oracles have particularly strong connections to other
topics, highlighting their multidisciplinary nature. Emerging keywords relate
to web and mobile apps, machine learning, energy consumption, automated program
repair and test generation, while emerging connections have formed between web
apps, test oracles, and machine learning with many topics. Random and
requirements-based testing show potential decline.
Conclusions: Our observations, advice, and map data offer a deeper
understanding of the field and inspiration regarding challenges and connections
to explore.Comment: To appear, Journal of Systems and Softwar
Transforming pre-service teacher curriculum: observation through a TPACK lens
This paper will discuss an international online collaborative learning experience through the lens of the Technological Pedagogical Content Knowledge (TPACK) framework. The teacher knowledge required to effectively provide transformative learning experiences for 21st century learners in a digital world is complex, situated and changing. The discussion looks beyond the opportunity for knowledge development of content, pedagogy and technology as components of TPACK towards the interaction between those three components. Implications for practice are also discussed. In todayâs technology infused classrooms it is within the realms of teacher educators, practising teaching and pre-service teachers explore and address effective practices using technology to enhance learning
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