579 research outputs found
On the real world practice of Behaviour Driven Development
Surveys of industry practice over the last decade suggest that Behaviour Driven Development is a popular Agile practice. For example, 19% of respondents to the 14th State of Agile annual survey reported using BDD, placing it in the top 13 practices reported. As well as potential benefits, the adoption of BDD necessarily involves an additional cost of writing and maintaining Gherkin features and scenarios, and (if used for acceptance testing,) the associated step functions. Yet there is a lack of published literature exploring how BDD is used in practice and the challenges experienced by real world software development efforts. This gap is significant because without understanding current real world practice, it is hard to identify opportunities to address and mitigate challenges. In order to address this research gap concerning the challenges of using BDD, this thesis reports on a research project which explored: (a) the challenges of applying agile and undertaking requirements engineering in a real world context; (b) the challenges of applying BDD specifically and (c) the application of BDD in open-source projects to understand challenges in this different context.
For this purpose, we progressively conducted two case studies, two series of interviews, four iterations of action research, and an empirical study. The first case study was conducted in an avionics company to discover the challenges of using an agile process in a large scale safety critical project environment. Since requirements management was found to be one of the biggest challenges during the case study, we decided to investigate BDD because of its reputation for requirements management. The second case study was conducted in the company with an aim to discover the challenges of using BDD in real life. The case study was complemented with an empirical study of the practice of BDD in open source projects, taking a study sample from the GitHub open source collaboration site.
As a result of this Ph.D research, we were able to discover: (i) challenges of using an agile process in a large scale safety-critical organisation, (ii) current state of BDD in practice, (iii) technical limitations of Gherkin (i.e., the language for writing requirements in BDD), (iv) challenges of using BDD in a real project, (v) bad smells in the Gherkin specifications of open source projects on GitHub. We also presented a brief comparison between the theoretical description of BDD and BDD in practice. This research, therefore, presents the results of lessons learned from BDD in practice, and serves as a guide for software practitioners planning on using BDD in their projects
Translating Islamic Law: the postcolonial quest for minority representation
This research sets out to investigate how culture-specific or signature concepts are rendered in English-language discourse on Islamic, or ‘shariʿa’ law, which has Arabic roots. A large body of literature has investigated Islamic law from a technical perspective. However, from the perspective of linguistics and translation studies, little attention has been paid to the lexicon that makes up this specialised discourse. Much of the commentary has so far been prescriptive, with limited empirical evidence. This thesis aims to bridge this gap by exploring how ‘culturalese’ (i.e., ostensive cultural discourse) travels through language, as evidenced in the self-built Islamic Law Corpus (ILC), a 9-million-word monolingual English corpus, covering diverse genres on Islamic finance and family law.
Using a mixed methods design, the study first quantifies the different linguistic strategies used to render shariʿa-based concepts in English, in order to explore ‘translation’ norms based on linguistic frequency in the corpus. This quantitative analysis employs two models: profile-based correspondence analysis, which considers the probability of lexical variation in expressing a conceptual category, and logistic regression (using MATLAB programming software), which measures the influence of the explanatory variables ‘genre’, ‘legal function’ and ‘subject field’ on the choice between an Arabic loanword and an endogenous English lexeme, i.e., a close English equivalent. The findings are then interpreted qualitatively in the light of postcolonial translation agendas, which aim to preserve intangible cultural heritage and promote the representation of minoritised groups.
The research finds that the English-language discourse on Islamic law is characterised by linguistic borrowing and glossing, implying an ideologically driven variety of English that can be usefully labelled as a kind of ‘Islamgish’ (blending ‘Islamic’ and ‘English’) aimed at retaining symbols of linguistic hybridity. The regression analysis confirms the influence of the above-mentioned contextual factors on the use of an Arabic loanword versus English alternatives
Tweeting through Turmoil: A Mixed Methods Exploration on the Impact of the COVID-19 Pandemic on Canadians' Discourse about Refugee Individuals
Canada's role in supporting the global refugee crisis has been significant. However, the COVID-19 pandemic has introduced new challenges, impacting the resettlement of refugee groups globally. Nationalist tendencies, already prevalent in populations, have been exacerbated, leading to increased biases against other groups and the reinforcement of borders in host nations. Negative representations of refugees can influence immigration policies, further restricting their admittance into host countries. To better understand public sentiment towards refugees, this mixed methods study looks at Canadian Twitter users’ online messages related to refugees. Employing an innovative computational quantitative methodology of sentiment analysis, the study evaluates shifts in public sentiment towards refugees. The results of the sentiment analysis reveal a significant shift in sentiment during the pandemic. Qualitative content analysis, along with critical discourse analysis, shed further light on themes that emerged from the tweets. These include pandemic-related discussions, increases in xenophobia, racism, and prejudice, and a decline in community support. By integrating the results of both quantitative and qualitative analyses, the study provides a comprehensive understanding of the shift in sentiment toward refugees during the pandemic. This research has implications for immigration policy, particularly concerning the resettlement of refugee groups, and contributes to the broader understanding of how public sentiment shapes responses to the global refugee crisis. Furthermore, the study highlights the potential of innovative methodologies like sentiment analysis to gauge public opinion on widely-used social media platforms
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Sonic heritage: listening to the past
History is so often told through objects, images and photographs, but the potential of sounds to reveal place and space is often neglected. Our research project ‘Sonic Palimpsest’1 explores the potential of sound to evoke impressions and new understandings of the past, to embrace the sonic as a tool to understand what was, in a way that can complement and add to our predominant visual understandings. Our work includes the expansion of the Oral History archives held at Chatham Dockyard to include women’s voices and experiences, and the creation of sonic works to engage the public with their heritage. Our research highlights the social and cultural value of oral history and field recordings in the transmission of knowledge to both researchers and the public. Together these recordings document how buildings and spaces within the dockyard were used and experienced by those who worked there. We can begin to understand the social and cultural roles of these buildings within the community, both past and present
Beyond Quantity: Research with Subsymbolic AI
How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately
II Simposio de Patrimonio Cultural ICOMOS España
Las actas recogen los trabajos expuestos en la Universidad Politécnica de Cartagena, sede principal del Simposio, por especialistas que generosamente compartieron su tiempo y conocimiento con más de 250 profesionales y personas estudiosas del patrimonio cultural que pudieron reunirse e intercambiar experiencias durante los tres días de duración del encuentro. Los 119 trabajos que conforman estas actas fueron cuidadosamente examinados por un Comité Científico formado exclusivamente por miembros de ICOMOS-España, personas expertas del más alto nivel en los diversos ámbitos del patrimonio cultural, que realizaron las tareas de supervisión de las comunicaciones de forma completamente voluntaria y altruista para garantizar su interés, vigencia y calidad. Los miembros de ICOMOS-España que la componen establecieron con gran acierto y sensibilidad unas líneas conceptuales transversales que, siempre respetando la diversidad temática de los trabajos presentados, sirvieran para poner de manifiesto las principales problemáticas que el patrimonio cultural afronta en la actualidad: éxitos y retos de la Convención del Patrimonio Mundial tras el 50 aniversario de andadura y los 40 de su adopción en España, energías renovables y cambio climático, patrimonios que merecen una atención especial como el agrícola o el industrial, etcHernández Navarro, Y. (2023). II Simposio de Patrimonio Cultural ICOMOS España. Editorial Universitat Politècnica de València. https://doi.org/10.4995/icomos2022.2022.1685
Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse
This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses.
This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups.
In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena
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