For large-scale IT corpora with hundreds of classes organized in a hierarchy, the task of accurat... more For large-scale IT corpora with hundreds of classes organized in a hierarchy, the task of accurate classification of classes at the higher level in the hierarchies is crucial to avoid errors propagating to the lower levels. In the business world, an efficient and explainable ML model is preferred over an expensive black-box model, especially if the performance increase is marginal. A current trend in the Natural Language Processing (NLP) community is towards employing huge pre-trained language models (PLMs) or what is known as selfattention models (e.g., BERT) for almost any kind of NLP task (e.g., question-answering, sentiment analysis, text classification). Despite the widespread use of PLMs and the impressive performance in a broad range of NLP tasks, there is a lack of a clear and well-justified need to as why these models are being employed for domain-specific text classification (TC) tasks, given the monosemic nature of specialized words (i.e., jargon) found in domain-specific text which renders the purpose of contextualized embeddings (e.g., PLMs) futile. In this paper, we compare the accuracies of some state-of-the-art (SOTA) models reported in the literature against a Linear SVM classifier and TFIDF vectorization model on three TC datasets. Results show a comparable performance for the LinearSVM. The findings of this study show that for domain-specific TC tasks, a linear model can provide a comparable, cheap, reproducible, and interpretable alternative to attention-based models.
The development of Big Data applications is not well-explored, to our knowledge. Embracing Big Da... more The development of Big Data applications is not well-explored, to our knowledge. Embracing Big Data in system building, questions arise as to how to elicit, specify, analyse, model, and document Big Data quality requirements. In our ongoing research, we explore a requirements modelling language for Big Data software applications. In this paper, we introduce QualiBD, a modelling tool that implements the proposed goal-oriented requirements language that facilitates the modelling of Big Data quality requirements.
Computer Science and Software Engineering, Nov 6, 2017
The process of teaching software architectures should go beyond abstract concepts (such as qualit... more The process of teaching software architectures should go beyond abstract concepts (such as quality attributes, architectural tactics, patterns, and methods) to getting students to recognise and implement them practically. Clearly, for this, project work is essential so as to familiarise students with the key technologies and tools. We note that "cloud technology", widely popular in industry for hosting business services, is quite suited to teaching about service-oriented architectures and micro-services. However, our analysis suggests that the use of cloud technology in software architecture (SA) courses is not very strong in tertiary institutions. Given the time constraints in SA courses, the learning curve on both administrative and technical aspects of the underlying infrastructure should arguably be minimised so as to enable focus on the core features of the course. In this paper, we share our experience on using IBM Bluemix in a half-term course on software architectures at the University of Western Ontario. In particular, we note that while students need to familiarise themselves with the technology and the opportunity it provides for supporting end-user services, the learning curve of Bluemix is gradual enough for students to accomplish creating plausible services in a real world environment. This paper describes a number of observations and lessons learnt from the points of view of both students and instructors.
Computer Science and Software Engineering, Nov 2, 2015
In this workshop, we describe software engineering challenges created by the introduction of Big ... more In this workshop, we describe software engineering challenges created by the introduction of Big Data, Data Science, and Continuous Development into corporate and research environments. Bloomberg Business reports "Data scientists are the new superheroes". The desire to get immediate answers to complex questions is driving software development in new directions. Basic principles like Database modeling, Object Oriented Design, and Quality Metrics are taking a back seat to demands for more answers in less time.
The emergence of pre-trained language models (PLMs) has shown great success in many Natural Langu... more The emergence of pre-trained language models (PLMs) has shown great success in many Natural Language Processing (NLP) tasks including text classification. Due to the minimal to no feature engineering required when using these models, PLMs are becoming the de facto choice for any NLP task. However, for domain-specific corpora (e.g., financial, legal, and industrial), fine-tuning a pre-trained model for a specific task has shown to provide a performance improvement. In this paper, we compare the performance of four different PLMs on three public domain-free datasets and a real-world dataset containing domainspecific words, against a simple SVM linear classifier with TFIDF vectorized text. The experimental results on the four datasets show that using PLMs, even fine-tuned, do not provide significant gain over the linear SVM classifier. Hence, we recommend that for text classification tasks, traditional SVM along with careful feature engineering can provide a cheaper and superior performance than PLMs.
Abstract We propose an approach, together with specific underlying techniques and a system to sup... more Abstract We propose an approach, together with specific underlying techniques and a system to support these, for eliciting models of software development processes. Software process elicitation involves: gathering process information from the agents involved in a ...
... A quantitative approach for analyzing the impact of tools on software productivity. Advisers:... more ... A quantitative approach for analyzing the impact of tools on software productivity. Advisers: NazimH. Madhavji, Authors: Tilmann Bruckhaus, Publication: · Doctoral Dissertation, ... top of page AUTHORS. Advisers, Nazim H. Madhavji No contact information provided yet. ...
P_ rogramming environments have recently received considerable attention among programmig en .sof... more P_ rogramming environments have recently received considerable attention among programmig en .software engineers. Thequalityof a programming environment depends heavily rO rN e_ on the logical and aesthetic consistency of the system'sparts. Three main fac-ronments ...
In this paper, we describe our ongoing research aimed at defining a Requirements Engineering Arte... more In this paper, we describe our ongoing research aimed at defining a Requirements Engineering Artefact Model (REAM) in the context of Big Data software applications. This model aims to provide a “big picture” of the Requirements Engineering work products created and used in Big Data software development projects. REAM are important tools that can be used as references for the definition of domain-specific RE models, system life-cycle processes and artefact-centered processes, currently bereft in the Big Data Software Engineering research.
INFOR: Information Systems and Operational Research, 1985
AbstractOne of tbe difficulties in current data flow machines is in the processing of arrays effi... more AbstractOne of tbe difficulties in current data flow machines is in the processing of arrays efficiently. A new data flow system organization, Vivaldi II, is described to solve this problem. It treats arrays as whole objects, andasaresult, reducestheflowof traffic of tokens through the system. The proposed architecture integrates some of tbe existing approaches used in data flow and array processors, to tbe handling of both scalar and structured objects.
Proceedings. 1988 International Conference on Computer Languages
Abstract The design of modules in modular languages, and the model of software development activi... more Abstract The design of modules in modular languages, and the model of software development activities portrayed by it, have been well received in batch environments. However, from their experience in the design of the MUPE-2 integrated programming ...
For large-scale IT corpora with hundreds of classes organized in a hierarchy, the task of accurat... more For large-scale IT corpora with hundreds of classes organized in a hierarchy, the task of accurate classification of classes at the higher level in the hierarchies is crucial to avoid errors propagating to the lower levels. In the business world, an efficient and explainable ML model is preferred over an expensive black-box model, especially if the performance increase is marginal. A current trend in the Natural Language Processing (NLP) community is towards employing huge pre-trained language models (PLMs) or what is known as selfattention models (e.g., BERT) for almost any kind of NLP task (e.g., question-answering, sentiment analysis, text classification). Despite the widespread use of PLMs and the impressive performance in a broad range of NLP tasks, there is a lack of a clear and well-justified need to as why these models are being employed for domain-specific text classification (TC) tasks, given the monosemic nature of specialized words (i.e., jargon) found in domain-specific text which renders the purpose of contextualized embeddings (e.g., PLMs) futile. In this paper, we compare the accuracies of some state-of-the-art (SOTA) models reported in the literature against a Linear SVM classifier and TFIDF vectorization model on three TC datasets. Results show a comparable performance for the LinearSVM. The findings of this study show that for domain-specific TC tasks, a linear model can provide a comparable, cheap, reproducible, and interpretable alternative to attention-based models.
The development of Big Data applications is not well-explored, to our knowledge. Embracing Big Da... more The development of Big Data applications is not well-explored, to our knowledge. Embracing Big Data in system building, questions arise as to how to elicit, specify, analyse, model, and document Big Data quality requirements. In our ongoing research, we explore a requirements modelling language for Big Data software applications. In this paper, we introduce QualiBD, a modelling tool that implements the proposed goal-oriented requirements language that facilitates the modelling of Big Data quality requirements.
Computer Science and Software Engineering, Nov 6, 2017
The process of teaching software architectures should go beyond abstract concepts (such as qualit... more The process of teaching software architectures should go beyond abstract concepts (such as quality attributes, architectural tactics, patterns, and methods) to getting students to recognise and implement them practically. Clearly, for this, project work is essential so as to familiarise students with the key technologies and tools. We note that "cloud technology", widely popular in industry for hosting business services, is quite suited to teaching about service-oriented architectures and micro-services. However, our analysis suggests that the use of cloud technology in software architecture (SA) courses is not very strong in tertiary institutions. Given the time constraints in SA courses, the learning curve on both administrative and technical aspects of the underlying infrastructure should arguably be minimised so as to enable focus on the core features of the course. In this paper, we share our experience on using IBM Bluemix in a half-term course on software architectures at the University of Western Ontario. In particular, we note that while students need to familiarise themselves with the technology and the opportunity it provides for supporting end-user services, the learning curve of Bluemix is gradual enough for students to accomplish creating plausible services in a real world environment. This paper describes a number of observations and lessons learnt from the points of view of both students and instructors.
Computer Science and Software Engineering, Nov 2, 2015
In this workshop, we describe software engineering challenges created by the introduction of Big ... more In this workshop, we describe software engineering challenges created by the introduction of Big Data, Data Science, and Continuous Development into corporate and research environments. Bloomberg Business reports "Data scientists are the new superheroes". The desire to get immediate answers to complex questions is driving software development in new directions. Basic principles like Database modeling, Object Oriented Design, and Quality Metrics are taking a back seat to demands for more answers in less time.
The emergence of pre-trained language models (PLMs) has shown great success in many Natural Langu... more The emergence of pre-trained language models (PLMs) has shown great success in many Natural Language Processing (NLP) tasks including text classification. Due to the minimal to no feature engineering required when using these models, PLMs are becoming the de facto choice for any NLP task. However, for domain-specific corpora (e.g., financial, legal, and industrial), fine-tuning a pre-trained model for a specific task has shown to provide a performance improvement. In this paper, we compare the performance of four different PLMs on three public domain-free datasets and a real-world dataset containing domainspecific words, against a simple SVM linear classifier with TFIDF vectorized text. The experimental results on the four datasets show that using PLMs, even fine-tuned, do not provide significant gain over the linear SVM classifier. Hence, we recommend that for text classification tasks, traditional SVM along with careful feature engineering can provide a cheaper and superior performance than PLMs.
Abstract We propose an approach, together with specific underlying techniques and a system to sup... more Abstract We propose an approach, together with specific underlying techniques and a system to support these, for eliciting models of software development processes. Software process elicitation involves: gathering process information from the agents involved in a ...
... A quantitative approach for analyzing the impact of tools on software productivity. Advisers:... more ... A quantitative approach for analyzing the impact of tools on software productivity. Advisers: NazimH. Madhavji, Authors: Tilmann Bruckhaus, Publication: · Doctoral Dissertation, ... top of page AUTHORS. Advisers, Nazim H. Madhavji No contact information provided yet. ...
P_ rogramming environments have recently received considerable attention among programmig en .sof... more P_ rogramming environments have recently received considerable attention among programmig en .software engineers. Thequalityof a programming environment depends heavily rO rN e_ on the logical and aesthetic consistency of the system'sparts. Three main fac-ronments ...
In this paper, we describe our ongoing research aimed at defining a Requirements Engineering Arte... more In this paper, we describe our ongoing research aimed at defining a Requirements Engineering Artefact Model (REAM) in the context of Big Data software applications. This model aims to provide a “big picture” of the Requirements Engineering work products created and used in Big Data software development projects. REAM are important tools that can be used as references for the definition of domain-specific RE models, system life-cycle processes and artefact-centered processes, currently bereft in the Big Data Software Engineering research.
INFOR: Information Systems and Operational Research, 1985
AbstractOne of tbe difficulties in current data flow machines is in the processing of arrays effi... more AbstractOne of tbe difficulties in current data flow machines is in the processing of arrays efficiently. A new data flow system organization, Vivaldi II, is described to solve this problem. It treats arrays as whole objects, andasaresult, reducestheflowof traffic of tokens through the system. The proposed architecture integrates some of tbe existing approaches used in data flow and array processors, to tbe handling of both scalar and structured objects.
Proceedings. 1988 International Conference on Computer Languages
Abstract The design of modules in modular languages, and the model of software development activi... more Abstract The design of modules in modular languages, and the model of software development activities portrayed by it, have been well received in batch environments. However, from their experience in the design of the MUPE-2 integrated programming ...
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