Implications of query caching for JXTA peers

This dissertation studies the caching of queries and how to cache in an efficient way, so that retrieving previously accessed data does not need any intermediary nodes between the data-source peer and the querying peer in super-peer P2P network. A precise algorithm was devised that demonstrated how queries can be deconstructed to provide greater flexibility for reusing their constituent elements. It showed how subsequent queries can make use of more than one previous query and any part of those queries to reconstruct direct data communication with one or more source peers that have supplied data previously. In effect, a new query can search and exploit the entire cached list of queries to construct the list of the data locations it requires that might match any locations previously accessed. The new method increases the likelihood of repeat queries being able to reuse earlier queries and provides a viable way of by-passing shared data indexes in structured networks. It could also increase the efficiency of unstructured networks by reducing traffic and the propensity for network flooding. In addition, performance evaluation for predicting query routing performance by using a UML sequence diagram is introduced. This new method of performance evaluation provides designers with information about when it is most beneficial to use caching and how the peer connections can optimize its exploitation

A Modified ACO-based Search Algorithm for Detecting Protein Functional Module From Protein Interaction Network

Recent high-throughput experiments have generated protein-protein interaction data on a genomic scale, yielding the complete protein-protein interaction network for several organisms. Various graph clustering algorithms have been applied to protein interaction networks for detecting protein functional modules. Although the previous algorithms are scalable and robust, their accuracy is still limited because of the complex connectivity found in protein interaction networks. The Ant Colony Optimization (ACO) Algorithm has been adapted for the protein functional module detection by modeling the problem as an optimization problem. The adapted ACO (ACO-PFMDA) has obtained feasible solution but not as magnificent as those reported in the literature. Some shortcomings were identified and addressed by proposing a Modified Ant Colony Optimization Algorithm (ACO-PFMDM), which introduces two new scheme for controlling the two main parameters of ACO to solve PFMDP. Experiments on one popular benchmark dataset namely "Saccharomyces cerevisiae" which taken from two popular databases DIP and MIPS has been performed. The experimental result have proved that ACO-PFMDM have improved the overall performance of protein functional module detection. The search process of ACO-PFMDM has converged effectively compared to some state-of-art algorithms. Moreover, the proposed dynamic update of the heuristic parameters based on entropy has generated high quality tours and it can guide ants toward the effective solutions space in the initial search stages

A review: Requirements prioritization criteria within collaboration perspective

The attributes or criteria used in the requirements prioritization process become an essential reference in calculating priorities. Most of the techniques are used to increase the value impacting business success. On the contrary, there are limitations on cost, time, and resources for developing software. Therefore, the requirements prioritization process often requires collaboration from the perspectives involved. So far, the pattern and basis have not been seen in the criteria used in the requirements prioritization process. Consequently, there need to be other factors that become a reference so that the selection of criteria is appropriate. This study identifies criteria based on the categorized perspectives of requirements prioritization. A systematic literature review presents criteria for prioritizing requirements from multiple collaborative perspectives. Findings show that the criteria in requirements prioritization can be classified into beneficial and non-beneficial, where business value and development cost are the most frequently used criteria. Furthermore, the involvement of multiple perspectives in requirements prioritization focuses on the client’s and developer’s perspectives. The findings also reveal that some of the challenges in the requirements prioritization process are biases by stakeholders, reducing pairwise comparison, and scalability. In the future, it will be investigated whether the selection of criteria correlated with stakeholder perspectives will increase the accuracy of priorities. Thus, the contribution of this paper is to recommend criteria from stakeholders’ perspectives

A decision tree approach based on BOCR for minimizing criteria in requirements prioritization

The requirements selection in the development of a software mostly requires a set of criteria. Determining the criteria used is often confusing because of the many criteria that must match with the characteristics of the project. This study introduces how to classify criteria based on benefits, opportunities, costs, risks (BOCR) to make the requirements prioritization process scalable. Project context characteristics and stakeholder perspectives are essential points discussed in this study because they are crucial in the requirements prioritization process. The criteria obtained from the literature review were followed by a survey to determine the importance of the criteria and their grouping in the BOCR using the decision tree method. There are 38 criteria and are grouped into four categories. There are two very significant criteria with a high level of importance, namely business value and stakeholder satisfaction. A decision tree based on BOCR can be used to classify the criteria for requirements prioritization. This research contributes to assisting software developers in finding and determining the criteria operated during the prioritization of requirements. Additionally, it is important to consider the project context and the collaboration the client and developer when prioritizing requirements

A review article on software effort estimation in agile methodology

Currently, Agile software development method has been commonly used in software development projects, and the success rate is higher than waterfall projects. The effort estimation in Agile is still a challenge because most existing means are developed based on the conventional method. Therefore, this study aimed to ascertain the software effort estimation method that is applied in Agile, the implementation approach, and the attributes that affect effort estimation. The results showed the top three estimation that is applied in Agile, are machine learning (37%), Expert Judgement (26%), and Algorithmic (21%). The implementation of all machine learning methods used a hybrid approach, which is a combination of machine learning and expert judgement, or a mix of two or more machine learning. Meanwhile, the implementation of effort estimation through a hybrid approach was only used in 47% of relevant articles. In addition, effort estimation in Agile involved twenty-four attributes, where Complexity, Experience, Size, and Time are the most commonly used and implemented

A Review Article on Software Effort Estimation in Agile Methodology

Currently, Agile software development method has been commonly used in software development projects, and the success rate is higher than waterfall projects. The effort estimation in Agile is still a challenge because most existing means are developed based on the conventional method. Therefore, this study aimed to ascertain the software effort estimation method that is applied in Agile, the implementation approach, and the attributes that affect effort estimation. The results showed the top three estimation that is applied in Agile, are machine learning (37%), Expert Judgement (26%), and Algorithmic (21%). The implementation of all machine learning methods used a hybrid approach, which is a combination of machine learning and expert judgement, or a mix of two or more machine learning. Meanwhile, the implementation of effort estimation through a hybrid approach was only used in 47% of relevant articles. In addition, effort estimation in Agile involved twenty-four attributes, where Complexity, Experience, Size, and Time are the most commonly used and implemented

Significant Factors in Agile Software Development of Effort Estimation

The Agile effort estimation involves project-related and people-related factors. This research objective is to find the factors that influence Agile effort estimation significantly through path analysis using a structural equation model. This research built an agile effort estimation path coefficient model from six constructs from theories and previous studies. Project-related factors represent by requirement and design implementation constructs. People-related factors are measured by the construct of experience, knowledge, and technical ability. The last construct is the effort itself. SmartPLS is employed for the confirmatory composite analysis and the structural model assessment. The confirmatory composite analysis indicated that all constructs are reliable and valid. Furthermore, the structural model assessment found that all factors of project-related constructs have a positive relationship and significant influence, showing a coefficient path value of 59.1% between requirement and design implementation constructs. All constructs represent people-related factors indicated by the coefficient path value of 67% between experience and knowledge, 42.6% between experience and technical ability, and 54.4% between knowledge and technical ability. In addition, all constructs proved influential simultaneously to effort by 31.1%. Positively contribute provided by requirement, experience, and technology’s ability. Significantly influenced provided by constructs of the developer’s knowledge and technical ability. The largest effect is given by technical ability, knowledge, and experience on medium and small scales. Contrarily, both constructs from project-related effects can be negligible because there was no influence. Based on the result, this study concludes that the significant factors in Agile effort estimation are technical ability, knowledge, and experience

Feasibility study for developing an event prioritizing system using CMSs

A content management system (CMS) is software that lets users make, modify, publish, and save digital information. These systems provide a piece of software called a plugin, model, or extension that plugs into users' sites and add new functionality or extends existing functionality on the user site. In current events, prioritization is done manually. When changes in event attributes like personnel, logistics, or money happen after the hand-prioritized list of activities is completed, it is not an easy task to reprioritize. By developing an event prioritization plugin, the organizations in need will use it and reduce time and money consumption. This study aims to recognize and evaluate the existing CMSs, their domains, and available plugins. Assess the availability of event prioritization plugins and recommend developing a plugin to help charity organizations to select the best team and resources and conduct the events effectively. The search is done in the area of the developed research questions. Search phrases containing pertinent keywords were used to find primary studies linked to CMSs categorized under journal articles, conference papers, seminars, symposiums, book chapters, and related CMSs websites

A proposed requirements prioritization model based on cost-value approach with collaboration perspective

Criteria analysis in requirement prioritization should always be transparent and holistic to increase the stakeholder’s satisfaction. Different stakeholders have their views on the same criteria, making it challenging to agree on a specific set of criterion weights. In software development, collaboration between client and developer is needed for requirements to be achieved. The cost-value approach to determining priority requirements requires a client perspective to determine the value and developer’s concept to ascertain costs, then form a cost-value diagram for analysis. Therefore, the research conducted has not been detailed using a cost-value approach involving a client perspective and a developer concept. Usually, the criteria and alternatives weighted by the decision-maker are vague, uncertain, and subjective. Furthermore, using fuzzy numbers help solve different types of uncertainty and conflicting requirements to produce reliable work. This study proposes increasing the weighting of criteria in the requirements prioritization technique, based on cost-value, by doing a collaboration perspective through Fuzzy AHP and TOPSIS using linguistic terms by presenting a proposed model

Requirement prioritization based on non-functional requirement classification using hierarchy analytic hierarchy process

This Requirement prioritization is the process in requirement engineering which in one stage in SDLC. Requirements engineering process aid to increase the excellence of software systems. Software system requirements are often classified as functional requirements (FR) or non-functional requirement (NFR). To produce a high-quality software system, both functional and non-functional requirement must be considered during requirement prioritization process. Most of the existing requirement prioritization method is only considering functional requirements since but neglecting the effect of NFR on specified FR. The aim of this paper to propose requirement prioritization technique that embed the non-functional requirements using existing RALIC dataset. Implementation of this paper, RALIC dataset hierarchy (a) use pairwise comparison and consistency ratio check for accuracy. Classified the percentage of NFR for hierarchy (a). Hierarchy (a) pairwise comparison result of weights (a.1) was (0.60) which is highest prioritized. Classified the NFR for hierarchy (a) requirements was efficiency (60%), security was (31%) and usability was (11%). Efficiency was the highest in hierarchy (a). Oppositely, highest percentage of NFR for hierarchy (a to j) was Portability requirement (92%) from hierarchy (e). The finding, when make the pairwise comparison for hierarchy (a to j) requirements input was (15, 051), in contrast, hierarchically pairwise comparison for (a to j) requirements input was (403). Therefore, hierarchically comparison can be reduced the number of requirements (97.33 %)