Figure 1 - uploaded by Byron Williams
Content may be subject to copyright.
Source publication
With today's ever increasing demands on software, software developers must produce software that can be changed without the risk of degrading the software architecture. Degraded software architecture is problematic because it makes the system more prone to defects and increases the cost of making future changes. The effects of making changes to sof...
Context in source publication
Context 1
... of the change. These attributes describe the overall characteristics of the change and its effect on the whole system and development environment. Figure 1 shows the general change characteristics. In the figure, the shapes with the bold outline are the general attributes, and the shapes with the dashed line are the values that can be selected for each attribute. The values that are highlighted in gray are measured using the Overall Impact Scale (Table 1). The ...
Similar publications
In order to be able to enter the software market and occupy privileged positions or at least prosper in the industry, it is necessary to please the customer for whom they work, this is achieved with the required quality of the products demanded. Quality is no longer a factor, but it has become one of the main competitive factors, without which ever...
Citations
... On the other hand, these changes also have significant time pressure associated with these due to which the developers cannot fully evaluate the impact of these crucial changes on system architecture leading to lower quality and making future changes even more unmanageable. So, it is important to focus on software architecture while dealing with changes [7,27]. Software architecture plays a significant role in understanding the system as it reflects different components of the system and relations between these components. ...
One of the major challenges for software developer is to fulfill the quality requirements of the software systems. This emphasis on software quality has some se-rious implications in terms of customer satisfaction and system acceptance. Due to its significance, it is also considered as one of the major challenges to be met by software developer since s/he is responsible for fulfilling the quality requirements of the software systems. One way to address this challenge is to adopt architecture based software de-velopment. Software architecture as an artifact can be used to deal with software quality attributes (QA) such as maintainability, performance and reliability. Recently, the fo-cus of business is transforming rapidly from manual to computer based automations. Resultantly, software intensive systems are becoming large and complex. This is enhanc-ing our emphasis on system quality which ultimately gives rise to the need for software quality maintenance. In order to optimize the quality of the software maintenance in a rapidly changing environment, study of its ripple effect is very significant. In this pa-per, a methodology for architecture based quality maintenance ripple effect determination and analysis is proposed. A QA's Property-component connectivity matrix and compo-nent connectivity matrix for ripple effect analysis is also described. The methodology is illustrated and evaluated by using web content extraction application architecture. The advantages of the proposed methodology are also presented.
... On the other hand, these changes also have significant time pressure associated with these due to which the developers cannot fully evaluate the impact of these crucial changes on system architecture leading to lower quality and making future changes even more unmanageable. As software architecture can be viewed as perhaps the first actual visualization of the system which has been built, it is really advantageous to focus on software architecture while dealing with changes [9]. Software architecture plays a significant role in understanding the system as it reflects different components of the system and relations between these components. ...
... On the other hand, these changes also have significant time pressure associated with these due to which the developers cannot fully evaluate the impact of these crucial changes on system architecture leading to lower quality and making future changes even more unmanageable. As software architecture can be viewed as perhaps the first actual visualization of the system which has been built, it is really advantageous to focus on software architecture while dealing with changes [9]. Software architecture plays a significant role in understanding the system as it reflects different components of the system and relations between these components. ...
The success of any software system is mainly depending on its flexible architecture and goals it will achieve for the end user. Different changes need to be brought in to the software due to the growing demand of hardware and software industry. Good software needs to incorporate these changes easily. Various researches have shown that the cost of the software maintenance is much higher than the cost of the software development. In the past the maintenance activity was supposed to be started once the software is delivered, but it's no more the same. The focus on software change is started along the system development life cycle initial stages. This is enhancing our emphasis on system maintainability. In order to optimize the quality of the software maintenance in a rapidly changing environment, study of its ripple effect is very significant. In this paper, a methodology for architecture based quality maintenance ripple effect determination and analysis is proposed. The methodology is illustrated and evaluated using mobile cash application architecture.
... For this reason, researchers are trying to reduce SM effort by automating SM task [7]. [8], highlights importance of change in software and how to characterize the effect of change on software such as the cause of the change, the type of the change that needs to be made, and the part of the system that requires change. However, no suggestions are put forward to recognize varieties of MTs. ...
Complex process of software modifications called software maintenance (SM), is a costly and time consuming task. Classification of maintenance request (MR) on the type MR which are corrective, adaptive, perfective or preventive. The maintenance type (MT) are important in keeping the quality factors of the system. However, classification of MT is difficult in nature and this affect maintainability of the system. Thus, there is a need for tool which is able to classify MRs into MT automatically. In this study, we present new result of combination texts of features during MR classification. Three different classifications techniques, namely Decision Tree, Naïve Bayesian and Logistic Regression are applied to perform classification. 600 MRs from Mozilla bug tracking system (BTS) are used as source of data. The results show that the model is able to classify MRs into MT with accuracy between 67.33 and 78.17%.
... Nedstam, et al. identified changes to be either architectural: affecting the structure of the system, functional; affecting only userobservable attributes, or somewhere in between; affecting both user-observable attributes and the system architecture [8]. In creating the change characterization scheme, all of the above approaches were considered along with others cited in a technical report [11], but only the features that pointed to a direct change to the software architecture were included. ...
... These sections provide a high-level description of the scheme's attributes. A comprehensive explanation of the creation of the scheme and a detailed description of the attributes and their importance is available in a technical report [11]. ...
With today's ever increasing demands on software, developers must produce software that can be changed without the risk of degrading the software architecture. Degraded software architecture is problematic because it makes the system more prone to defects and increases the cost of making future changes. The effects of making changes to software can be difficult to measure. One way to address software changes is to characterize their causes and effects. This paper introduces an initial architecture change characterization scheme created to assist developers in measuring the impact of a change on the architecture of the system. It also presents an initial study conducted to gain insight into the validity of the scheme. The results of this study indicated a favorable view of the viability of the scheme by the subjects, and the scheme increased the ability of novice developers to assess and adequately estimate change effort.
one of the major challenges for software developer is to fulfill the quality requirements of the software systems. This emphasis on, software quality has some serious implications in terms of customer satisfaction and system acceptance. On the other hand, software architecture as an artifact can be used to deal with software qualities such as maintainability, performance and reliability etc. With rapid expansion on software systems to cater to the need of automation, software intensive systems are becoming large and complex. This is enhancing our emphasis on system quality which ultimately gives rise to the need for software quality maintenance. This software quality maintenance is not without ripple effect cost. This paper is an attempt to analyse architecture based quality maintenance ripple effect and for this purpose, a process for architecture based quality maintenance ripple effect analysis is proposed. A technique for ripple effect analysis is described.
The goal of this paper is to improve the prediction performance of fault-prone module prediction models (fault-proneness models) by employing over/under sampling methods, which are preprocessing procedures for a fit dataset. The sampling methods are expected to improve prediction performance when the fit dataset is unbalanced, i.e. there exists a large difference between the number of fault-prone modules and not-fault-prone modules. So far, there has been no research reporting the effects of applying sampling methods to fault-proneness models. In this paper, we experimentally evaluated the effects of four sampling methods (random over sampling, synthetic minority over sampling, random under sampling and one-sided selection) applied to four fault-proneness models (linear discriminant analysis, logistic regression analysis, neural network and classification tree) by using two module sets of industry legacy software. All four sampling methods improved the prediction performance of the linear and logistic models, while neural network and classification tree models did not benefit from the sampling methods. The improvements of Fl-values in linear and logistic models were 0.078 at minimum, 0.224 at maximum and 0.121 at the mean.
