11,553 research outputs found
From a Domain Analysis to the Specification and Detection of Code and Design Smells
Code and design smells are recurring design problems in software systems that must be identified to avoid their possible negative consequences\ud
on development and maintenance. Consequently, several smell detection\ud
approaches and tools have been proposed in the literature. However,\ud
so far, they allow the detection of predefined smells but the detection\ud
of new smells or smells adapted to the context of the analysed systems\ud
is possible only by implementing new detection algorithms manually.\ud
Moreover, previous approaches do not explain the transition from\ud
specifications of smells to their detection. Finally, the validation\ud
of the existing approaches and tools has been limited on few proprietary\ud
systems and on a reduced number of smells. In this paper, we introduce\ud
an approach to automate the generation of detection algorithms from\ud
specifications written using a domain-specific language. This language\ud
is defined from a thorough domain analysis. It allows the specification\ud
of smells using high-level domain-related abstractions. It allows\ud
the adaptation of the specifications of smells to the context of\ud
the analysed systems.We specify 10 smells, generate automatically\ud
their detection algorithms using templates, and validate the algorithms\ud
in terms of precision and recall on Xerces v2.7.0 and GanttProject\ud
v1.10.2, two open-source object-oriented systems.We also compare\ud
the detection results with those of a previous approach, iPlasma
A Domain Analysis to Specify Design Defects and Generate Detection Algorithms
Quality experts often need to identify in software systems design defects, which are recurring design problems, that hinder development\ud
and maintenance. Consequently, several defect detection approaches\ud
and tools have been proposed in the literature. However, we are not\ud
aware of any approach that defines and reifies the process of generating\ud
detection algorithms from the existing textual descriptions of defects.\ud
In this paper, we introduce an approach to automate the generation\ud
of detection algorithms from specifications written using a domain-specific\ud
language. The domain-specific is defined from a thorough domain analysis.\ud
We specify several design defects, generate automatically detection\ud
algorithms using templates, and validate the generated detection\ud
algorithms in terms of precision and recall on Xerces v2.7.0, an\ud
open-source object-oriented system
Contemporary Approach for Technical Reckoning Code Smells Detection using Textual Analysis
Software Designers should be aware of address design smells that can evident as results of design and decision. In a software project, technical debt needs to be repaid habitually to avoid its accretion. Large technical debt significantly degrades the quality of the software system and affects the productivity of the development team. In tremendous cases, when the accumulated technical reckoning becomes so enormous that it cannot be paid off to any further extent the product has to be abandoned. In this paper, we bridge the gap analyzing to what coverage abstract information, extracted using textual analysis techniques, can be used to identify smells in source code. The proposed textual-based move toward for detecting smells in source code, fabricated as TACO (Textual Analysis for Code smell detection), has been instantiated for detecting the long parameter list smell and has been evaluated on three sampling Java open source projects. The results determined that TACO is able to indentified between 50% and 77% of the smell instances with a exactitude ranging between 63% and 67%. In addition, the results show that TACO identifies smells that are not recognized by approaches based on exclusively structural information
Motivation, Design, and Ubiquity: A Discussion of Research Ethics and Computer Science
Modern society is permeated with computers, and the software that controls
them can have latent, long-term, and immediate effects that reach far beyond
the actual users of these systems. This places researchers in Computer Science
and Software Engineering in a critical position of influence and
responsibility, more than any other field because computer systems are vital
research tools for other disciplines. This essay presents several key ethical
concerns and responsibilities relating to research in computing. The goal is to
promote awareness and discussion of ethical issues among computer science
researchers. A hypothetical case study is provided, along with questions for
reflection and discussion.Comment: Written as central essay for the Computer Science module of the
LANGURE model curriculum in Research Ethic
Towards a Smarter organization for a Self-servicing Society
Traditional social organizations such as those for the management of
healthcare are the result of designs that matched well with an operational
context considerably different from the one we are experiencing today. The new
context reveals all the fragility of our societies. In this paper, a platform
is introduced by combining social-oriented communities and complex-event
processing concepts: SELFSERV. Its aim is to complement the "old recipes" with
smarter forms of social organization based on the self-service paradigm and by
exploring culture-specific aspects and technological challenges.Comment: Final version of a paper published in the Proceedings of
International Conference on Software Development and Technologies for
Enhancing Accessibility and Fighting Info-exclusion (DSAI'16), special track
on Emergent Technologies for Ambient Assisted Living (ETAAL
A Multi-Level Framework for the Detection, Prioritization and Testing of Software Design Defects
Large-scale software systems exhibit high complexity and become difficult to maintain. In fact, it has been reported that software cost dedicated to maintenance and evolution activities is more
than 80% of the total software costs. In particular, object-oriented software systems need to
follow some traditional design principles such as data abstraction, encapsulation, and modularity.
However, some of these non-functional requirements can be violated by developers for many
reasons such as inexperience with object-oriented design principles, deadline stress. This high
cost of maintenance activities could potentially be greatly reduced by providing automatic or
semi-automatic solutions to increase system‟s comprehensibility, adaptability and extensibility to
avoid bad-practices.
The detection of refactoring opportunities focuses on the detection of bad smells, also called
antipatterns, which have been recognized as the design situations that may cause software
failures indirectly. The correction of one bad smell may influence other bad smells. Thus, the
order of fixing bad smells is important to reduce the effort and maximize the refactoring benefits.
However, very few studies addressed the problem of finding the optimal sequence in which the
refactoring opportunities, such as bad smells, should be ordered. Few other studies tried to
prioritize refactoring opportunities based on the types of bad smells to determine their severity.
However, the correction of severe bad smells may require a high effort which should be
optimized and the relationships between the different bad smells are not considered during the
prioritization process.
The main goal of this research is to help software engineers to refactor large-scale systems with a
minimum effort and few interactions including the detection, management and testing of
refactoring opportunities. We report the results of an empirical study with an implementation of
our bi-level approach. The obtained results provide evidence to support the claim that our
proposal is more efficient, on average, than existing techniques based on a benchmark of 9 open
source systems and 1 industrial project. We have also evaluated the relevance and usefulness of
the proposed bi-level framework for software engineers to improve the quality of their systems
and support the detection of transformation errors by generating efficient test cases.Ph.D.Information Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136075/1/Dilan_Sahin_Final Dissertation.pdfDescription of Dilan_Sahin_Final Dissertation.pdf : Dissertatio
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