Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userEvaluation of the Design Metric to Reduce the Number of Defects in Software Development
194
0
0.0
(
0
)
Added by
M. Rizwan Jameel Qureshi Dr.
Publication date
2012
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Software design is one of the most important and key activities in the system development life cycle (SDLC) phase that ensures the quality of software. Different key areas of design are very vital to be taken into consideration while designing software. Software design describes how the software system is decomposed and managed in smaller components. Object-oriented (OO) paradigm has facilitated software industry with more reliable and manageable software and its design. The quality of the software design can be measured through different metrics such as Chidamber and Kemerer (CK) design metrics, Mood Metrics & Lorenz and Kidd metrics. CK metrics is one of the oldest and most reliable metrics among all metrics available to software industry to evaluate OO design. This paper presents an evaluation of CK metrics to propose an improved CK design metrics values to reduce the defects during software design phase in software. This paper will also describe that whether a significant effect of any CK design metrics exists on total number of defects per module or not. This is achieved by conducting survey in two software development companies.
rate research
Read More
Pushed by market forces, software development has become fast-paced. As a consequence, modern development projects are assembled from 3rd-party components. Security & privacy assurance techniques once designed for large, controlled updates over months or years, must now cope with small, continuous changes taking place within a week, and happening in sub-components that are controlled by third-party developers one might not even know they existed. In this paper, we aim to provide an overview of the current software security approaches and evaluate their appropriateness in the face of the changed nature in software development. Software security assurance could benefit by switching from a process-based to an artefact-based approach. Further, security evaluation might need to be more incremental, automated and decentralized. We believe this can be achieved by supporting mechanisms for lightweight and scalable screenings that are applicable to the entire population of software components albeit there might be a price to pay.
Because of the importance of object oriented methodologies, the research in developing new measure for object oriented system development is getting increased focus. The most of the metrics need to find the interactions between the objects and modules for developing necessary metric and an influential software measure that is attracting the software developers, designers and researchers. In this paper a new interactions are defined for object oriented system. Using these interactions, a parser is developed to analyze the existing architecture of the software. Within the design model, it is necessary for design classes to collaborate with one another. However, collaboration should be kept to an acceptable minimum i.e. better designing practice will introduce low coupling. If a design model is highly coupled, the system is difficult to implement, to test and to maintain overtime. In case of enhancing software, we need to introduce or remove module and in that case coupling is the most important factor to be considered because unnecessary coupling may make the system unstable and may cause reduction in the systems performance. So coupling is thought to be a desirable goal in software construction, leading to better values for external software qualities such as maintainability, reusability and so on. To test this hypothesis, a good measure of class coupling is needed. In this paper, based on the developed tool called Design Analyzer we propose a methodology to reuse an existing system with the objective of enhancing an existing Object oriented system keeping the coupling as low as possible.
Because of the importance of object oriented methodologies, the research in developing new measure for object oriented system development is getting increased focus. The most of the metrics need to find the interactions between the objects and modules for developing necessary metric and an influential software measure that is attracting the software developers, designers and researchers. In this paper a new interactions are defined for object oriented system. Using these interactions, a parser is developed to analyze the existing architecture of the software. Within the design model, it is necessary for design classes to collaborate with one another. However, collaboration should be kept to an acceptable minimum i.e. better designing practice will introduce low coupling. If a design model is highly coupled, the system is difficult to implement, to test and to maintain overtime. In case of enhancing software, we need to introduce or remove module and in that case coupling is the most important factor to be considered because unnecessary coupling may make the system unstable and may cause reduction in the systems performance. So coupling is thought to be a desirable goal in software construction, leading to better values for external software qualities such as maintainability, reusability and so on. To test this hypothesis, a good measure of class coupling is needed. In this paper, based on the developed tool called Design Analyzer we propose a methodology to reuse an existing system with the objective of enhancing an existing Object oriented system keeping the coupling as low as possible.
Software Heritage is the largest existing public archive of software source code and accompanying development history. It spans more than five billion unique source code files and one billion unique commits , coming from more than 80 million software projects. These software artifacts were retrieved from major collaborative development platforms (e.g., GitHub, GitLab) and package repositories (e.g., PyPI, Debian, NPM), and stored in a uniform representation linking together source code files, directories, commits, and full snapshots of version control systems (VCS) repositories as observed by Software Heritage during periodic crawls. This dataset is unique in terms of accessibility and scale, and allows to explore a number of research questions on the long tail of public software development, instead of solely focusing on most starred repositories as it often happens.
Over the past decade, deep learning (DL) has been successfully applied to many industrial domain-specific tasks. However, the current state-of-the-art DL software still suffers from quality issues, which raises great concern especially in the context of safety- and security-critical scenarios. Adversarial examples (AEs) represent a typical and important type of defects needed to be urgently addressed, on which a DL software makes incorrect decisions. Such defects occur through either intentional attack or physical-world noise perceived by input sensors, potentially hindering further industry deployment. The intrinsic uncertainty nature of deep learning decisions can be a fundamental reason for its incorrect behavior. Although some testing, adversarial attack and defense techniques have been recently proposed, it still lacks a systematic study to uncover the relationship between AEs and DL uncertainty. In this paper, we conduct a large-scale study towards bridging this gap. We first investigate the capability of multiple uncertainty metrics in differentiating benign examples (BEs) and AEs, which enables to characterize the uncertainty patterns of input data. Then, we identify and categorize the uncertainty patterns of BEs and AEs, and find that while BEs and AEs generated by existing methods do follow common uncertainty patterns, some other uncertainty patterns are largely missed. Based on this, we propose an automated testing technique to generate multiple types of uncommon AEs and BEs that are largely missed by existing techniques. Our further evaluation reveals that the uncommon data generated by our method is hard to be defended by the existing defense techniques with the average defense success rate reduced by 35%. Our results call for attention and necessity to generate more diverse data for evaluating quality assurance solutions of DL software.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
