January 2009
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29 Reads
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3 Citations
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January 2009
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29 Reads
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3 Citations
January 2009
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12 Reads
January 2009
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4 Reads
November 2008
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32 Reads
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31 Citations
Lecture Notes in Computer Science
In collocated software development teams, informal communication is the key enabler for sharing knowledge. In distributed teams, development infrastructures have to fill communication gaps with light-weight articulation and sharing facilities for evolving development knowledge. We propose an ontology-based framework to capture, access and share developers’ experiences in a decentralized, contextualized manner. Capturing developers’ interaction with related artifacts and providing a Wiki-like annotation approach triggers knowledge capture. Integrated semantic search and recommendation fosters knowledge access and sharing. Our framework enables distributed teams to become more effective by learning from each other’s experiences, e.g. on reusing specific components and handling semantic errors.
November 2008
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66 Reads
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57 Citations
By surveying recommendation systems in software develop-ment, we found that existing approaches have been focusing on "you might like what similar developers like" scenarios. However structured artifacts and semantically well-defined development activities bear large potentials for further rec-ommendation scenarios. We introduce a novel "landscape" of software development recommendation systems and line out several scenarios for knowledge sharing and collabora-tion. Basic challenges are improving context-awareness and particularly addressing information providers.
October 2008
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41 Reads
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5 Citations
MaRK_08 focuses on potentials and benefits of lightweight knowledge management approaches, such as ontology-based annotation, semantic Wikis and rationale management techniques, applied to requirements engineering. Methodologies, processes and tools for capturing, externalizing, sharing and reusing of knowledge in (distributed) requirements engineering processes are discussed. Furthermore, the workshop is an interactive exchange platform between the knowledge management community, requirements engineering community and industrial practitioners. This proceeding includes selected and refereed contributions.
May 2008
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47 Reads
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6 Citations
Proceedings - International Conference on Software Engineering
Large software development projects are complex endeavours that involve numerous participants which can work across several sites and act in various roles. Each participant produces and consumes information relevant for the success of the project. In such settings, an effective and efficient allocation of knowledge is a hard challenge, especially if there is no central authority, which enforces standards for the whole ecosystem. We consider semantic technologies as an important enabler to improve information and knowledge sharing in such scenarios, by helping to exchange and interconnect software engineering knowledge across the web. In this paper, we describe the corresponding vision of a Software Engineering Semantic Web and the role of intelligent IDEs in order to benefit from and contribute to it.
January 2008
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18 Reads
January 2008
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320 Reads
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1 Citation
January 2008
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563 Reads
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23 Citations
... According to the results, suggestions are less likely to be used when looking for a suitable reviewer, an expert's help or when the goal is to learn how code review is conducted in a project (22/92, 20/92 and 14/92 positive answers respectively). We conclude that suggestions are used as an information source, mainly when looking for rationale or for a code example [35], [36]. ...
December 2024
ACM Transactions on Software Engineering and Methodology
... However, current UI retrieval methods are limited in their ability to capture the nuanced semantic elements essential for an effective, inspirational design process. Prior research [22,52] has focused on pixel-level retrieval methods, emphasizing visual similarities. While these approaches are effective for identifying surface-level design patterns, they often overlook in-depth semantic aspects such as user interaction flow, target audience, and the emotional tone of an application. ...
November 2024
ACM Transactions on Software Engineering and Methodology
... Additionally, we believe the perception of GAI and ChatGPT in particular should be investigated further, to understand what motivates the students' behavior and impact their prompting skills [19]. In our study, we created an environment that was as close to the students' regular working environment as possible, to elicit their typical behavior. ...
October 2024
... This approach, despite its inherent challenges, offers considerable advantages by leveraging the unique capabilities of both humans and AI technologies. A major issue, however, lies in achieving a unified understanding of the diverse research fields that apply the concept of collaboration, especially in pinpointing the specific attributes that characterize collaboration within each field: centered approach to collaboration by directly involving individuals in AI systems' decision-making processes [29,30,31]. • Interactive Machine Learning (IML): Highlights the importance of creating interactive communication channels between users and AI models, emphasizing user engagement throughout various stages of AI development [32]. ...
January 2023
IEEE Software
... This gets reflected in the diversity of feedback and submitting users. Demographic attributes like primary language, experience, culture, communication channels, and even personality traits [6] might have an impact on the feedback content and linguistic properties as recent research shows [25,20]. Therefore, whenever possible, such metadata about submitting users should be collected or extracted from the text. ...
September 2023
... Furthermore, concerns were raised about the intrusiveness of bots as in the following responses: "Bots can be spammy"," they would be rather annoying if they are too extensive or trivial", "[...] it could easily be off-putting and spammy if a bot quickly creates hundreds of suggestions." Others were also concerned about the precision of a potential automation stating that they are " prone to false-positives, which make automated code review suggestions less useful on Based on the results from our study and the need for "smarter bots" outlined in the work of Wessel et al. [39], we recommend integrating context-awareness for automation efforts using code review suggestions [5], [40]. Such efforts should leverage project and developer attributes to provide targeted support and avoid spamming code review discussions. ...
May 2023
... Similarly, researchers [31,32,36,42] also conducted semi-structured interviews for requirements elicitation. Additionally, there are studies [47][48][49]52] concentrated on requirements elicitation that prioritize stakeholders' perspectives and needs. ...
April 2023
Computer
... Empirical research on software documentation quality is an active field that focuses on various artifacts, like API reference documentation [40] or README files [68], and the perspectives of documentation writers [1]. Studies on the evaluation of AI-generated documentation usually focus on automated metrics like BLEU, ROUGE, and METEOR [29], [64], [69]- [71]. ...
October 2022
... For example, Nicholson and Guo [33] encoded both the textual data (title and description, using TF-IDF or fastText embeddings trained by Wikipedia, StackOverflow, or project-specific documents) and the metadata (issue type, reporter identifier and assignee identifier using one-hot encoding). Lüders et al. [20], on the other hand, chose to encode issues only using their title and description (through general BERT model 15 ) considering those are universal features across different issue trackers. Then, a pair of artifacts can be represented as a combination of the features of individual artifacts, with optionally other features on the relations of the two involved artifacts (such as differences in creation time between the two issues). ...
August 2022
... into a query or an issue report, to derive an appropriate reaction strategy. A bot would also immediately engage with the user to collect missing details thus improving the feedback quality whenever needed [48,88]. In case of an inquiry, e.g., on how to perform a certain task with the app or how to configure the system for enabling or disabling certain features, the bot can try to answer directly or delegate to the support teams if not possible or not satisfactory. ...
August 2022