| 11. |
- Abdeen, Waleed, et al.
(författare)
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Multi-Label Requirements Classification with Large Taxonomies
- 2024
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Ingår i: Proceedings of the IEEE International Conference on Requirements Engineering. - : IEEE Computer Society. - 9798350395112 ; , s. 264-274
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Konferensbidrag (refereegranskat)abstract
- Context and motivation: Classification aids software development activities by organizing requirements in classes for easier access and retrieval. The majority of requirements classification research has, so far, focused on binary or multi-class classification. Question/problem: Multi-label classification with large taxonomies could aid requirements traceability but is prohibitively costly with supervised training. Hence, we investigate zero-short learning to evaluate the feasibility of multi-label requirements classification with large taxonomies. Principal ideas/results: We associated, together with domain experts from the industry, 129 requirements with 769 labels from taxonomies ranging between 250 and 1183 classes. Then, we conducted a controlled experiment to study the impact of the type of classifier, the hierarchy, and the structural characteristics of taxonomies on the classification performance. The results show that: (1) The sentence-based classifier had a significantly higher recall compared to the word-based classifier; however, the precision and F1-score did not improve significantly. (2) The hierarchical classification strategy did not always improve the performance of requirements classification. (3) The total and leaf nodes of the taxonomies have a strong negative correlation with the recall of the hierarchical sentence-based classifier. Contribution: We investigate the problem of multi-label requirements classification with large taxonomies, illustrate a systematic process to create a ground truth involving industry participants, and provide an analysis of different classification pipelines using zero-shot learning. © 2024 IEEE.
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| 12. |
- Abdeen, Waleed
(författare)
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Reducing the Distance Between Requirements Engineering and Verification
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Background Requirements engineering and verification (REV) processes play es-sential roles in software product development. There are physical and non-physicaldistances between entities (actors, artifacts, and activities) in these processes. Cur-rent practices that reduce the distances, such as automated testing and alignmentof document structure and tracing only partially close the above mentioned gap.Objective The aim of this thesis is to investigate solutions w.r.t their abilityto reduce the distances between requirements engineering and verification. Twotechniques that are explored in this thesis are automated testing (model-basedtesting, MBT) and alignment of document structure and tracing (traceability).Method The research methods used in this thesis are systematic mapping, soft-ware requirements mining, case study, literature survey, validation study, and de-sign science.Results MBT and traceability are effective in reducing the distance between re-quirements and verification. However, both activities have some shortcoming thatneeds to be addressed when used for that purpose. Current MBT techniques inthe context of software performance do not attain all the goals of MBT: 1) require-ments validation, 2) checking the testability of requirements, and 3) the generationof an efficient test suite. These goals are essential to reduce the distance. We de-veloped and assessed performance requirements verification and test environmentgeneration approach to tackle these shortcomings. Also, traceability between re-quirements and verification suffers from the low granularity of trace links and doesnot support the verification of all requirements. We propose the use of taxonomictrace links to trace and align the structure of requirements specifications and ver-ification artifacts. The results from the validation study show that the solution isfeasible in practice. However, this comes with challenges that need to be addressed.Conclusion MBT and improved traceability reduce multiple distances betweenactors, artifacts, and activities in the requirements engineering and verificationprocess. MBT is most effective in reducing the distances when the model used isbuilt from the requirements. Traceability is essential in easing access to relevantinformation when needed and should not be seen as an overhead. When creatingtrace links, we need to consider the difference in the abstraction, structure, andtime between the linked artifacts
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| 13. |
- Abdeen, Waleed
(författare)
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Taxonomic Trace Links Recommender : Context Aware Hierarchical Classification
- 2023
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Ingår i: CEUR Workshop Proceedings. - : CEUR-WS.
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Konferensbidrag (refereegranskat)abstract
- In the taxonomic trace links concept, the source and target artifacts are connected through knowledge organization structure (e.g., taxonomy). We introduce in this paper a recommender system that recommends labels to requirements artifacts from domain-specific taxonomy to establish taxonomic trace links. The tool exploits the hierarchical nature of taxonomies and uses requirements text and context information as input to the recommender. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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| 14. |
- Abdeen, Waleed, et al.
(författare)
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Taxonomic Trace Links - Rethinking Traceability and its Benefits
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Background: Traceability is an important quality of artifacts that are used in knowledge-intensive tasks. When projectbudgets and time pressure are a reality, this leads often to a down-prioritization of creating trace links. Objective:We propose a new idea that uses knowledge organization structures, such as taxonomies, ontologies and thesauri, asan auxiliary artifact to establish trace links. In order to investigate the novelty and feasibility of this idea, we studytraceability in the area of requirements engineering. Method: First, we conduct a literature survey to investigate towhat extent and how auxiliary artifacts have been used in the past for requirements traceability. Then, we conduct avalidation study in industry, testing the idea of taxonomic trace links with realistic artifacts. Results: We have reviewed126 studies that investigate requirements traceability; ninetey-one of them use auxiliary artifacts in the traceabilityprocess. In the validation study, while we have encountered six challenges when classifying requirements with a domain-specific taxonomy, we found that designers and engineers are able to classify design objects comprehensively and reliably.Conclusions: The idea of taxonomic trace links is novel and feasible in practice. However, the identified challenges needto be addressed to allow for an adoption in practice and enable a transfer to software intensive contexts.
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| 15. |
- Abdelraheem, Mohamed Ahmed, et al.
(författare)
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Executing Boolean queries on an encrypted Bitmap index
- 2016
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Ingår i: CCSW 2016 - Proceedings of the 2016 ACM Cloud Computing Security Workshop, co-located with CCS 2016. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450345729 ; , s. 11-22
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Konferensbidrag (refereegranskat)abstract
- We propose a simple and efficient searchable symmetric encryption scheme based on a Bitmap index that evaluates Boolean queries. Our scheme provides a practical solution in settings where communications and computations are very constrained as it offers a suitable trade-off between privacy and performance.
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| 16. |
- Abghari, Shahrooz, et al.
(författare)
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A Higher Order Mining Approach for the Analysis of Real-World Datasets
- 2020
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Ingår i: Energies. - : MDPI. - 1996-1073. ; 13:21
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Tidskriftsartikel (refereegranskat)abstract
- In this study, we propose a higher order mining approach that can be used for the analysis of real-world datasets. The approach can be used to monitor and identify the deviating operational behaviour of the studied phenomenon in the absence of prior knowledge about the data. The proposed approach consists of several different data analysis techniques, such as sequential pattern mining, clustering analysis, consensus clustering and the minimum spanning tree (MST). Initially, a clustering analysis is performed on the extracted patterns to model the behavioural modes of the studied phenomenon for a given time interval. The generated clustering models, which correspond to every two consecutive time intervals, can further be assessed to determine changes in the monitored behaviour. In cases in which significant differences are observed, further analysis is performed by integrating the generated models into a consensus clustering and applying an MST to identify deviating behaviours. The validity and potential of the proposed approach is demonstrated on a real-world dataset originating from a network of district heating (DH) substations. The obtained results show that our approach is capable of detecting deviating and sub-optimal behaviours of DH substations.
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| 17. |
- Abghari, Shahrooz, et al.
(författare)
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A Minimum Spanning Tree Clustering Approach for Outlier Detection in Event Sequences
- 2018
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Ingår i: The 17th IEEE International Conference on Machine Learning and Applications Special Session on Machine Learning Algorithms, Systems and Applications. - : IEEE. ; , s. 1123-1130
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Konferensbidrag (refereegranskat)abstract
- Outlier detection has been studied in many domains. Outliers arise due to different reasons such as mechanical issues, fraudulent behavior, and human error. In this paper, we propose an unsupervised approach for outlier detection in a sequence dataset. The proposed approach combines sequential pattern mining, cluster analysis, and a minimum spanning tree algorithm in order to identify clusters of outliers. Initially, the sequential pattern mining is used to extract frequent sequential patterns. Next, the extracted patterns are clustered into groups of similar patterns. Finally, the minimum spanning tree algorithm is used to find groups of outliers. The proposed approach has been evaluated on two different real datasets, i.e., smart meter data and video session data. The obtained results have shown that our approach can be applied to narrow down the space of events to a set of potential outliers and facilitate domain experts in further analysis and identification of system level issues.
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| 18. |
- Abghari, Shahrooz, et al.
(författare)
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An Inductive System Monitoring Approach for GNSS Activation
- 2022
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Ingår i: IFIP Advances in Information and Communication Technology. - Cham : Springer Science+Business Media B.V.. - 9783031083365 ; , s. 437-449
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Konferensbidrag (refereegranskat)abstract
- In this paper, we propose a Global Navigation Satellite System (GNSS) component activation model for mobile tracking devices that automatically detects indoor/outdoor environments using the radio signals received from Long-Term Evolution (LTE) base stations. We use an Inductive System Monitoring (ISM) technique to model environmental scenarios captured by a smart tracker via extracting clusters of corresponding value ranges from LTE base stations’ signal strength. The ISM-based model is built by using the tracker’s historical data labeled with GPS coordinates. The built model is further refined by applying it to additional data without GPS location collected by the same device. This procedure allows us to identify the clusters that describe semi-outdoor scenarios. In that way, the model discriminates between two outdoor environmental categories: open outdoor and semi-outdoor. The proposed ISM-based GNSS activation approach is studied and evaluated on a real-world dataset contains radio signal measurements collected by five smart trackers and their geographical location in various environmental scenarios.
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| 19. |
- Abghari, Shahrooz
(författare)
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Data Mining Approaches for Outlier Detection Analysis
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Outlier detection is studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. Outlier detection techniques are often domain-specific. The main challenge in outlier detection relates to modelling the normal behaviour in order to identify abnormalities. The choice of model is important, i.e., an unsuitable data model can lead to poor results. This requires a good understanding and interpretation of the data, the constraints, and requirements of the domain problem. Outlier detection is largely an unsupervised problem due to unavailability of labeled data and the fact that labeled data is expensive. In this thesis, we study and apply a combination of both machine learning and data mining techniques to build data-driven and domain-oriented outlier detection models. We focus on three real-world application domains: maritime surveillance, district heating, and online media and sequence datasets. We show the importance of data preprocessing as well as feature selection in building suitable methods for data modelling. We take advantage of both supervised and unsupervised techniques to create hybrid methods. More specifically, we propose a rule-based anomaly detection system using open data for the maritime surveillance domain. We exploit sequential pattern mining for identifying contextual and collective outliers in online media data. We propose a minimum spanning tree clustering technique for detection of groups of outliers in online media and sequence data. We develop a few higher order mining approaches for identifying manual changes and deviating behaviours in the heating systems at the building level. The proposed approaches are shown to be capable of explaining the underlying properties of the detected outliers. This can facilitate domain experts in narrowing down the scope of analysis and understanding the reasons of such anomalous behaviours. We also investigate the reproducibility of the proposed models in similar application domains.
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| 20. |
- Abghari, Shahrooz
(författare)
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Data Modeling for Outlier Detection
- 2018
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis explores the data modeling for outlier detection techniques in three different application domains: maritime surveillance, district heating, and online media and sequence datasets. The proposed models are evaluated and validated under different experimental scenarios, taking into account specific characteristics and setups of the different domains.Outlier detection has been studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. Outlier detection techniques are often domain-specific. The main challenge in outlier detection relates to modeling the normal behavior in order to identify abnormalities. The choice of model is important, i.e., an incorrect choice of data model can lead to poor results. This requires a good understanding and interpretation of the data, the constraints, and the requirements of the problem domain. Outlier detection is largely an unsupervised problem due to unavailability of labeled data and the fact that labeled data is expensive.We have studied and applied a combination of both machine learning and data mining techniques to build data-driven and domain-oriented outlier detection models. We have shown the importance of data preprocessing as well as feature selection in building suitable methods for data modeling. We have taken advantage of both supervised and unsupervised techniques to create hybrid methods. For example, we have proposed a rule-based outlier detection system based on open data for the maritime surveillance domain. Furthermore, we have combined cluster analysis and regression to identify manual changes in the heating systems at the building level. Sequential pattern mining for identifying contextual and collective outliers in online media data have also been exploited. In addition, we have proposed a minimum spanning tree clustering technique for detection of groups of outliers in online media and sequence data. The proposed models have been shown to be capable of explaining the underlying properties of the detected outliers. This can facilitate domain experts in narrowing down the scope of analysis and understanding the reasons of such anomalous behaviors. We have also investigated the reproducibility of the proposed models in similar application domains.
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