scholarly journals A Prediction Method for Evolution Effects of Software Architecture

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Tong Wang ◽  
Yebin Chen ◽  
Xiaoyan Wang
Keyword(s):  
Software Architecture ◽  
Software Quality ◽  
Software Maintenance ◽  
Prediction Method ◽  
Maintenance Cost ◽  
Software Performance ◽  
Bp Network ◽  
Architecture Evolution ◽  
Evolution Effect ◽  
Combined Measurements

Software architecture evolution may lead to architecture erosion, resulting in the increase of software maintenance cost, the deterioration of software quality, the decline of software performance, and so on. In order to avoid software architecture erosion, we should evaluate the evolution effect of software architecture in time. This paper proposes a prediction method for the evolution effects of software architecture based on BP network. Firstly, this method proposes four evolution principles and evaluates the overall evolution effects based on the combined measurements. Then, we extract the evolutionary activities from release notes. Finally, we establish a prediction model for evolution effect based on BP network. Experimental results show that the proposed method can be used to predict the evolution effect.

Evolution Impact on Architecture Stability in Open-Source Projects

2015 ◽  
Vol 5 (4) ◽  
pp. 24-35 ◽  
Author(s):  
Mamdouh Alenezi ◽  
Fakhry Khellah
Keyword(s):  
Software Architecture ◽  
Open Source ◽  
Open Source Software ◽  
Quality Standard ◽  
Management Style ◽  
Efficient Technique ◽  
Maintenance Cost ◽  
Software Systems ◽  
Architecture Evolution

Software systems usually evolve constantly, which requires constant development and maintenance. Subsequently, the architecture of these systems tends to degrade with time. Therefore, stability is a key measure for evaluating an architecture. Open-source software systems are becoming progressively vital these days. Since open-source software systems are usually developed in a different management style, the quality of their architectures needs to be studied. ISO/IEC SQuaRe quality standard characterized stability as one of the sub-characteristics of maintainability. Unstable software architecture could cause the software to require high maintenance cost and effort. In this work, the authors propose a simple, yet efficient, technique that is based on carefully aggregating the package level stability in order to measure the change in the architecture level stability as the architecture evolution happens. The proposed method can be used to further study the cause behind the positive or negative architecture stability changes.

Financial market trend analysis based on autoregressive conditional heteroscedasticity model and BP neural network prediction

2021 ◽  
pp. 002072092098849
Author(s):  
Xinzhe Yin ◽  
Jinghua Li
Keyword(s):  
Neural Network ◽  
Trend Analysis ◽  
Financial Market ◽  
Prediction Method ◽  
Good Effect ◽  
Trend Prediction ◽  
Bp Network ◽  
Conditional Heteroscedasticity ◽  
Bp Model ◽  
Autoregressive Conditional Heteroscedasticity

Many experts and scholars at home and abroad have studied this topic in depth, laying a solid foundation for the research of financial market prediction. At present, the mainstream prediction method is to use neural network and autoregressive conditional heteroscedasticity to build models, which is a more scientific way, and also verified the feasibility of the way in many studies. In order to improve the accuracy of financial market trend prediction, this paper studies in detail the neural network system represented by BP and the autoregressive conditional heterogeneous variance model represented by GARCH. Analyze its structure and algorithm, combine the advantages of both, create a GARCH-BP model, and transform its combination structure and optimize the algorithm according to the uniqueness of the financial market, so as to meet the market as much as possible Characteristics. The novelty of this paper is the construction of the autoregressive conditional heteroscedasticity model, which lays the foundation for the prediction of financial market trends through the construction of the model. However, there are some shortcomings in this article. The overall overview of the financial market is not very clear, and the prediction of the BP network is not so comprehensive. Finally, through the actual data statistics of market transactions, the effectiveness of the GARCH-BP model was tested, analyzed and researched. The final results show that model has a good effect on the prediction and trend analysis of market, and its accuracy and availability greatly improved compared with the previous conventional approach, which is worth further study and extensive research It is believed that the financial market prediction model will become one of the mainstream tools in the industry after its later improvement.

scholarly journals Same but Different: Consistently Developing and Evolving Software Architecture Models and Their Implementation

2020 ◽  
pp. 87-112
Author(s):  
Marco Konersmann ◽  
Michael Goedicke
Keyword(s):  
Performance Evaluation ◽  
Software Architecture ◽  
Software Quality ◽  
Source Code ◽  
Use Case ◽  
Program Code ◽  
Separate Representation ◽  
Main Driver ◽  
Integrated Architecture ◽  
Architecture Specification

AbstractAs software architecture is a main driver for the software quality, source code is often accompanied by software architecture specifications. When the implementation is changed, the architecture specification is often not updated along with the code, which introduces inconsistencies between these artifacts. Such inconsistencies imply a risk of misunderstandings and errors during the development, maintenance, and evolution, causing serious degradation over the lifetime of the system. In this chapter we present the Explicitly Integrated Architecture approach and its tool Codeling, which remove the necessity for a separate representation of software architecture by integrating software architecture information with the program code. By using our approach, the specification can be extracted from the source code and changes in the specification can be propagated to the code. The integration of architecture information with the code leaves no room for inconsistencies between the artifacts and creates links between artifacts. We evaluate the approach and tool in a use case with real software in development and with a benchmark software, accompanied by a performance evaluation.

Prediction of Change-Prone Classes Using Machine Learning and Statistical Techniques

2014 ◽  
pp. 193-202
Author(s):  
LinRuchika Malhotra ◽  
Ankita Jain Bansal
Keyword(s):  
Machine Learning ◽  
Software Quality ◽  
Software Maintenance ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Change Proneness ◽  
Regression Techniques ◽  
Reducing Costs ◽  
Boosting Technique ◽  
The Relationship

For software development, availability of resources is limited, thereby necessitating efficient and effective utilization of resources. This can be achieved through prediction of key attributes, which affect software quality such as fault proneness, change proneness, effort, maintainability, etc. The primary aim of this chapter is to investigate the relationship between object-oriented metrics and change proneness. Predicting the classes that are prone to changes can help in maintenance and testing. Developers can focus on the classes that are more change prone by appropriately allocating resources. This will help in reducing costs associated with software maintenance activities. The authors have constructed models to predict change proneness using various machine-learning methods and one statistical method. They have evaluated and compared the performance of these methods. The proposed models are validated using open source software, Frinika, and the results are evaluated using Receiver Operating Characteristic (ROC) analysis. The study shows that machine-learning methods are more efficient than regression techniques. Among the machine-learning methods, boosting technique (i.e. Logitboost) outperformed all the other models. Thus, the authors conclude that the developed models can be used to predict the change proneness of classes, leading to improved software quality.

R4 Model for Case-Based Reasoning and Its Application for Software Fault Prediction

2016 ◽  
Vol 8 (3) ◽  
pp. 19-38 ◽  
Author(s):  
Ekbal Rashid
Keyword(s):  
Software Quality ◽  
Similarity Measures ◽  
Fault Prediction ◽  
Maintenance Cost ◽  
Quality Level ◽  
Case Based Reasoning ◽  
Quality Prediction ◽  
Error Level ◽  
Software Fault Prediction ◽  
Case Based

Making R4 model effective and efficient I have introduced some new features, i.e., renovation of knowledgebase (KBS) and reducing the maintenance cost by removing the duplicate record from the KBS. Renovation of knowledgebase is the process of removing duplicate record stored in knowledgebase and adding world new problems along with world new solutions. This paper explores case-based reasoning and its applications for software quality improvement through early prediction of error patterns. It summarizes a variety of techniques for software quality prediction in the domain of software engineering. The system predicts the error level with respect to LOC and with respect to development time, and both affects the quality level. This paper also reviews four existing models of case-based reasoning (CBR). The paper presents a work in which I have expanded our previous work (Rashid et al., 2012). I have used different similarity measures to find the best method that increases reliability. The present work is also credited through introduction of some new terms like coefficient of efficiency, i.e., developer's ability.

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