| 1. |
- Battistoni, Giuseppe, et al.
(författare)
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FLUKA Capabilities and CERN Applications for the Study of Radiation Damage to Electronics at High-Energy Hadron Accelerators
- 2011
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Konferensbidrag (refereegranskat)abstract
- The assessment of radiation damage to electronics is a complex process and requires a detailed description of the full particle energy spectra, as well as a clear characterization of the quantities used to predict radiation damage. FLUKA, a multi-purpose particle interaction and transport code, is capable of calculating proton-proton and heavy ion collisions at LHC energies and beyond. It correctly describes the entire hadronic and electromagnetic particle cascade initiated by secondary particles from TeV energies down to thermal neutrons, and provides direct scoring capabilities essential to estimate in detail the possible risk of radiation damage to electronics. This paper presents the FLUKA capabilities for applications related to radiation damage to electronics, providing benchmarking examples and showing the practical applications of FLUKA at CERN facilities such as CNGS and LHC. Related applications range from the study of device effects, the detailed characterization of the radiation field and radiation monitor calibration, to the input requirements for important mitigation studies including shielding, relocation or other options.
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| 2. |
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| 3. |
- Abbas, Muhammad, et al.
(författare)
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Is Requirements Similarity a Good Proxy for Software Similarity? : An Empirical Investigation in Industry
- 2021
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Ingår i: <em>Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) </em>27th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2021, 12 April 2021 - 15 April 2021. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030731274 ; , s. 3-18, s. 3-18
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Konferensbidrag (refereegranskat)abstract
- [Context and Motivation] Content-based recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a new requirement is proposed by a stakeholder, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn identify previously developed code. [Question/problem] Several NLP approaches for similarity computation are available, and there is little empirical evidence on the adoption of an effective technique in recommender systems specifically oriented to requirements-based code reuse. [Principal ideas/results] This study compares different state-of-the-art NLP approaches and correlates the similarity among requirements with the similarity of their source code. The evaluation is conducted on real-world requirements from two industrial projects in the railway domain. Results show that requirements similarity computed with the traditional tf-idf approach has the highest correlation with the actual software similarity in the considered context. Furthermore, results indicate a moderate positive correlation with Spearman’s rank correlation coefficient of more than 0.5. [Contribution] Our work is among the first ones to explore the relationship between requirements similarity and software similarity. In addition, we also identify a suitable approach for computing requirements similarity that reflects software similarity well in an industrial context. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and categorization.
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| 4. |
- Abbas, Muhammad, et al.
(författare)
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On the relationship between similar requirements and similar software : A case study in the railway domain
- 2023
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Ingår i: Requirements Engineering. - : Springer Science and Business Media Deutschland GmbH. - 0947-3602 .- 1432-010X. ; 28, s. 23-47
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Tidskriftsartikel (refereegranskat)abstract
- Recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a stakeholder proposes a new requirement, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn, identify previously developed code. Several NLP approaches for similarity computation between requirements are available. However, there is little empirical evidence on their effectiveness for code retrieval. This study compares different NLP approaches, from lexical ones to semantic, deep-learning techniques, and correlates the similarity among requirements with the similarity of their associated software. The evaluation is conducted on real-world requirements from two industrial projects from a railway company. Specifically, the most similar pairs of requirements across two industrial projects are automatically identified using six language models. Then, the trace links between requirements and software are used to identify the software pairs associated with each requirements pair. The software similarity between pairs is then automatically computed with JPLag. Finally, the correlation between requirements similarity and software similarity is evaluated to see which language model shows the highest correlation and is thus more appropriate for code retrieval. In addition, we perform a focus group with members of the company to collect qualitative data. Results show a moderately positive correlation between requirements similarity and software similarity, with the pre-trained deep learning-based BERT language model with preprocessing outperforming the other models. Practitioners confirm that requirements similarity is generally regarded as a proxy for software similarity. However, they also highlight that additional aspect comes into play when deciding software reuse, e.g., domain/project knowledge, information coming from test cases, and trace links. Our work is among the first ones to explore the relationship between requirements and software similarity from a quantitative and qualitative standpoint. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and change impact analysis.
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| 5. |
- Barbariga, Marco, et al.
(författare)
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Ceruloplasmin functional changes in Parkinson's disease-cerebrospinal fluid.
- 2015
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Ingår i: Molecular Neurodegeneration. - : Springer Science and Business Media LLC. - 1750-1326. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Ceruloplasmin, a ferroxidase present in cerebrospinal fluid (CSF), plays a role in iron homeostasis protecting tissues from oxidative damage. Its reduced enzymatic activity was reported in Parkinson's disease (PD) contributing to the pathological iron accumulation. We previously showed that ceruloplasmin is modified by oxidation in vivo, and, in addition, in vitro by deamidation of specific NGR-motifs that foster the gain of integrin-binding function. Here we investigated whether the loss of ceruloplasmin ferroxidase activity in the CSF of PD patients was accompanied by NGR-motifs deamidation and gain of function.
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| 6. |
- Bashir, Sarmad, et al.
(författare)
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Requirements Classification for Smart Allocation : A Case Study in the Railway Industry
- 2023
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Ingår i: 31st IEEE International Requirements Engineering Conference. - Hannover, Germany : IEEE.
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Konferensbidrag (refereegranskat)abstract
- Allocation of requirements to different teams is a typical preliminary task in large-scale system development projects. This critical activity is often performed manually and can benefit from automated requirements classification techniques. To date, limited evidence is available about the effectiveness of existing machine learning (ML) approaches for requirements classification in industrial cases. This paper aims to fill this gap by evaluating state-of-the-art language models and ML algorithms for classification in the railway industry. Since the interpretation of the results of ML systems is particularly relevant in the studied context, we also provide an information augmentation approach to complement the output of the ML-based classification. Our results show that the BERT uncased language model with the softmax classifier can allocate the requirements to different teams with a 76% F1 score when considering requirements allocation to the most frequent teams. Information augmentation provides potentially useful indications in 76% of the cases. The results confirm that currently available techniques can be applied to real-world cases, thus enabling the first step for technology transfer of automated requirements classification. The study can be useful to practitioners operating in requirements-centered contexts such as railways, where accurate requirements classification becomes crucial for better allocation of requirements to various teams.
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| 7. |
- Dekhtyar, Alexander, et al.
(författare)
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Requirements engineering (RE) for social good: RE cares [requirements]
- 2019
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Ingår i: IEEE Software. - 0740-7459 .- 1937-4194. ; 36:1
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Tidskriftsartikel (refereegranskat)abstract
- © 1984-2012 IEEE. As researchers and teachers and practitioners, we software types excel at multitasking. This, in part, led us to ask the question: Can one attend a software engineering conference and do something good for society? We found the answer to be a resounding yes. In this article, we present our first experience of running RE Cares, a conference collocated event. This event included a workshop, conference sessions, and a hackathon for developing an application to support emergency field activity for Mutual Aid Alberta, a nonprofit organization coordinating natural disaster responses in the Canadian province.
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| 8. |
- Ferrari, Alessio, et al.
(författare)
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Preface
- 2023
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 1611-3349 .- 0302-9743. ; 13975 LNCS, s. v-vii
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Konferensbidrag (refereegranskat)
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| 9. |
- Ferrari, Alessio, et al.
(författare)
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Using Voice and Biofeedback to Predict User Engagement during Product Feedback Interviews
- 2024
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Ingår i: ACM Transactions on Software Engineering and Methodology. - : ACM Digital Library. - 1049-331X .- 1557-7392. ; 33:4
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Tidskriftsartikel (refereegranskat)abstract
- Capturing users’ engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collecting and analyzing users’ feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in contexts where online feedback is limited, as for the majority of apps, and software in general. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this article, we propose to utilize biometric data, in terms of physiological and voice features, to complement product feedback interviews with information about the engagement of the user on product-relevant topics. We evaluate our approach by interviewing users while gathering their physiological data (i.e., biofeedback) using an Empatica E4 wristband, and capturing their voice through the default audio-recorder of a common laptop. Our results show that we can predict users’ engagement by training supervised machine learning algorithms on biofeedback and voice data, and that voice features alone can be sufficiently effective. The best configurations evaluated achieve an average F1 ∼ 70% in terms of classification performance, and use voice features only. This work is one of the first studies in requirements engineering in which biometrics are used to identify emotions. Furthermore, this is one of the first studies in software engineering that considers voice analysis. The usage of voice features can be particularly helpful for emotion-aware feedback collection in remote communication, either performed by human analysts or voice-based chatbots, and can also be exploited to support the analysis of meetings in software engineering research. © 2024 Copyright held by the owner/author(s).
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| 10. |
- Habibullah, Khan Mohammad, 1990, et al.
(författare)
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Requirements Engineering for Automotive Perception Systems: An Interview Study
- 2023
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 1611-3349 .- 0302-9743. - 9783031297854 ; 13975 LNCS, s. 189-205
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Konferensbidrag (refereegranskat)abstract
- Background: Driving automation systems (DAS), including autonomous driving and advanced driver assistance, are an important safety-critical domain. DAS often incorporate perceptions systems that use machine learning (ML) to analyze the vehicle environment. Aims: We explore new or differing requirements engineering (RE) topics and challenges that practitioners experience in this domain. Method: We have conducted an interview study with 19 participants across five companies and performed thematic analysis. Results: Practitioners have difficulty specifying upfront requirements, and often rely on scenarios and operational design domains (ODDs) as RE artifacts. Challenges relate to ODD detection and ODD exit detection, realistic scenarios, edge case specification, breaking down requirements, traceability, creating specifications for data and annotations, and quantifying quality requirements. Conclusions: Our findings contribute to understanding how RE is practiced for DAS perception systems and the collected challenges can drive future research for DAS and other ML-enabled systems.
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