“…While formal concept analysis is based on the notion of formal context to our knowledge there is only limited work that can handle the application context of signifiers/attributes and objects relating them over multiple formal contexts. 5 We agree with a conclusion that Snelting [14] made when he surveyed the use of FCA in software analysis. He states that:…”
Section: Opportunities and Challenges For Formal Concept Analysissupporting
confidence: 88%
“…Formal concept analysis has been successfully used in several stages of the software engineering lifecycle. For example, Hesse and Tilley [5] describe the use of formal concept analysis in the early stages of the software development process. Core is the elucidation and formation of important programming concepts, such as classes and components: "FCA allows a "crossing of perspectives' -between the functional view represented by the use cases and the data view implied by the 'things' occurring there.…”
Section: Concepts In Programming Languages Using Queriesmentioning
Formal concept analysis (FCA) derives a hierarchy of concepts in a formal context that relates objects with attributes. This approach is very well aligned with the traditions of Frege, Saussure and Peirce, which relate a signifier (e.g. a word/an attribute) to a mental concept evoked by this word and meant to refer to a specific object in the real world. However, in the practice of natural languages as well as artificial languages (e.g. programming languages), the application context often constitutes a latent variable that influences the interpretation of a signifier. We present some of our current work that analyzes the usage of words in natural language in varying application contexts as well as the usage of variables in programming languages in varying application contexts in order to provide conceptual constraints on these signifiers.
“…While formal concept analysis is based on the notion of formal context to our knowledge there is only limited work that can handle the application context of signifiers/attributes and objects relating them over multiple formal contexts. 5 We agree with a conclusion that Snelting [14] made when he surveyed the use of FCA in software analysis. He states that:…”
Section: Opportunities and Challenges For Formal Concept Analysissupporting
confidence: 88%
“…Formal concept analysis has been successfully used in several stages of the software engineering lifecycle. For example, Hesse and Tilley [5] describe the use of formal concept analysis in the early stages of the software development process. Core is the elucidation and formation of important programming concepts, such as classes and components: "FCA allows a "crossing of perspectives' -between the functional view represented by the use cases and the data view implied by the 'things' occurring there.…”
Section: Concepts In Programming Languages Using Queriesmentioning
Formal concept analysis (FCA) derives a hierarchy of concepts in a formal context that relates objects with attributes. This approach is very well aligned with the traditions of Frege, Saussure and Peirce, which relate a signifier (e.g. a word/an attribute) to a mental concept evoked by this word and meant to refer to a specific object in the real world. However, in the practice of natural languages as well as artificial languages (e.g. programming languages), the application context often constitutes a latent variable that influences the interpretation of a signifier. We present some of our current work that analyzes the usage of words in natural language in varying application contexts as well as the usage of variables in programming languages in varying application contexts in order to provide conceptual constraints on these signifiers.
“…Although the approach of converting formal concepts into classes has been successfully used by others (Hesse and Tilley 2005;Godin and Valtchev 2005), it is not valid in our context. This approach is based on the analysis of the extents of the formal concepts.…”
Section: From Entities To Componentsmentioning
confidence: 99%
“…Regarding related work, we can mention other applications of FCA to software engineering. The work described in (Hesse and Tilley 2005) focuses on the use of FCA during the early phases of software development. They propose a method for finding or deriving class candidates from a given use case description.…”
Due to the changing nature of videogames, the component-based architecture is the design of choice for managing game entities instead of the traditional static class hierarchies. A component-based architecture lets programmers edit entities as collections of components, which provide the entity with new functionalities. Such architecture promotes flexibility but makes the code more difficult to understand because entities are built at runtime by linking components. In this paper we present a semi-automatic process for moving from a class hierarchy to a component-based architecture. Through the application of Formal Concept Analysis we propose a novel technique for automatically identifying candidate distributions of responsibilities among components.
“…For example, in our case study we observed that the concept 'buffer' was interleaved (lines 330 to 336, 348, 354, 357 and 360 to 366) in EditPane.java. Hence, to overcome the interleaving issue we use Concept Lattice, a proven technique used to represent conceptual hierarchies [8]. Using the inherent ordering of the lattice structure, we extract code lexicons to synthesize a summary.…”
Developers need to understand many Software Engineering (SE) artifacts while making changes to the code. In such cases, developers use cues extensively to establish relevance of an information with the task. Their familiarity with different kind of cues will help them in comprehending a program. But, developers face information overload because (a) there are many cues and (b) they might be unfamiliar with artifacts. So, we propose a novel approach to overcome information overload problem by modeling developer's perceived value of information based on cues. In this preliminary study, we validate one such model for common comprehension tasks. We also apply this model to summarize source code. An evaluation of the generated summaries resulted in 83% similarity with summaries recorded by developers. The promising results encourages us to create a repository of perception models that can later aid complex SE tasks.
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