Maintenance decision making in problem and modification analysis phase using a knowledge-based model

Software cost and time estimation is the process of estimating the cost and time required to develop a software system. Software cost and time estimation supports the planning and tracking of software projects. Effectively controlling the expensive investment of software development is one of the important issues in software project management. Estimating software development cost with high precision is still a great challenge for project managers, because it allows for considerable financial and strategic planning. Software cost estimation refers to the predictions of the likely amount of effort, time, and staffing levels required to build a software system. A very helpful form of cost estimation is the one made at an early stage during a project, when the costing of the project is proposed for approval. However, estimates at the early stages of the development are the most difficult to obtain. In this paper a novel Constructive Cost Model (COCOMO) based on soft computing approach is proposed for software cost estimation. This model carries some of the desirable features of neural networks approach, such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. Unlike the standard neural networks approach, the proposed model can be interpreted and validated by experts, and has good generalisation capability. The model deals effectively with imprecise and uncertain input and enhances the reliability of software cost estimates. From the experimental results, it was concluded that, by the proposed neural network model, the accuracy of cost estimation can be improved and the estimated cost can be very close to the actual cost.

The recent advancements in information and communication technology (ICT) have inspired all the operational domains of both public and private sector enterprise to endorse this technology. Software development plays a crucial role in supporting ICT. Software effort estimation serves as a critical factor in software application development, and it helps application development teams to complete the development process on time and within budget. Many developmental approaches have been used for software effort estimation, but most of them were conventional software methods and therefore failed to produce accurate results when it came to web or mobile effort estimation. This chapter explains different types of software applications, software estimation models, the importance of software effort estimation, and challenges faced in software effort estimation.

The Software Engineering Institute's Softwares Process Maturity Model was developed to provide a basis for assessing an organization's ability to perform software engineering activities. It also provides a guide for improving an organization's software engineering capability. The maturity model was designed on the basis of concepts associated with developing new, large, complex systems. This has led to difficulties in applying the model to organizations involved entirely in software maintenance. The author addresses the tailoring issues encountered in applying the software maturity model to a large maintenance organization at Paramax Space Systems Operation. It is concluded that the issues that arise when the Capability Maturity Model is applied to a software sustaining engineering organization are significant but do not prohibit the use of the model as a guide for software process improvement. Issues mainly arise with the treatment requirements and differences in project management needs. The primary tailoring issues are ones of wording as opposed to applicability to maintenance activities. >

Crop pests are among the greatest threats to food security, generating broad economic, social, and environmental impacts. These pests interact with their hosts and the environment through complex pathways, and it is increasingly common to find professionals from different areas gathering into projects that attempt to deal with this complexity. We propose a framework called FramePests guiding steps and activities for crop pest modeling and forecasting. From theoretical references about carrying out mappings and systematic reviews of the literature, the framework proposes a series of steps leading to a state of science as a knowledge base for modeling tasks. Then, two modeling solutions, based on data and knowledge are used. Finally, the model outputs and performances are compared. The application of the proposed framework was demonstrated for coffee leaf rust modeling, for which we obtained a data-based model built using a gradient boosting algorithm ( XGBoost ) with a mean absolute error of 7.19% and a knowledge-based model represented by a hierarchical multi-criteria decision structure with an accuracy of 56.03%. A complementary study for our case study allowed us to explore how elements of a data-based model can improve a knowledge-based model, improving its accuracy by 7.07%. and showed that knowledge-based modeling can be an alternative to data-based modeling when the available dataset has approximately 60 instances. Data-based models tend to have better performance, but their replicability is conditioned by the diversity in the dataset used. Knowledge-based models may be simpler but allow expert supervision, and these models are not usually tied to specific sites.

Nowadays, software effort estimation plays an important role in software project management due to its extensive use in industry to monitor progress, and performance, determine overall productivity and assist in project planning. After the success of methods such as IFPUG Function Point Analysis, MarkII Function Point Analysis, and COSMIC Full Function Points, several other extension methods have been introduced to be adopted in software projects. Despite the efficiency in measuring the software cost, software effort estimation, unfortunately, is facing several issues; it requires some knowledge, effort, and a significant amount of time to conduct the measurement, thus slightly ruining the advantages of this approach. This paper demonstrates a functional size measurement tool, named UML Point tool, that utilizes the concept of IFPUG Function Point Analysis directly to Unified Modeling Language (UML) model. The tool allows the UML eXchange Format (UXF) file to decode the UML model of mobile game requirement and extract the diagrams into component complexity, object interface complexity, and sequence diagram complexity, according to the defined measurement rules. UML Point tool then automatically compute the functional size, effort, time, human resources, and total development cost of mobile game. Besides, this paper also provides a simple case study to validate the tool. The initial results proved that the tool could be useful to improve estimation accuracy for mobile game application development and found to be reliable to be applied in the mobile game industry.

BackgroundAceTree, a software application first released in 2006, facilitates exploration, curation and editing of tracked C. elegans nuclei in 4-dimensional (4D) fluorescence microscopy datasets. Since its initial release, AceTree has been continuously used to interact with, edit and interpret C. elegans lineage data. In its 11 year lifetime, AceTree has been periodically updated to meet the technical and research demands of its community of users. This paper presents the newest iteration of AceTree which contains extensive updates, demonstrates the new applicability of AceTree in other developmental contexts, and presents its evolutionary software development paradigm as a viable model for maintaining scientific software.ResultsLarge scale updates have been made to the user interface for an improved user experience. Tools have been grouped according to functionality and obsolete methods have been removed. Internal requirements have been changed that enable greater flexibility of use both in C. elegans contexts and in other model organisms. Additionally, the original 3-dimensional (3D) viewing window has been completely reimplemented. The new window provides a new suite of tools for data exploration.ConclusionBy responding to technical advancements and research demands, AceTree has remained a useful tool for scientific research for over a decade. The updates made to the codebase have extended AceTree’s applicability beyond its initial use in C. elegans and enabled its usage with other model organisms. The evolution of AceTree demonstrates a viable model for maintaining scientific software over long periods of time.

SummaryAn emergency event is an unexceptional event that exceeds the capacity of normal resources and organization to cope and a situation that poses an immediate risk to health, life, property, or environment. Crowdsourcing connects unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods, to create solutions that improve urban environment, human life quality, and city operation systems. The crowdsourcing on social media can be used to detect and analyze urban emergency events. In this paper, in order to detect and describe the real‐time urban emergency event, the knowledge base model is proposed. The crowdsourcing‐based knowledge base model is firstly introduced, which uses the information from social media. Secondly, the basic definition of the proposed knowledge base model including keywords, patterns, positive sentences, and knowledge graph is given. Thirdly, the temporal information is added to the proposed knowledge base model. The case study on real data sets shows that the proposed algorithm has good performance and high effectiveness in the analysis and detection of emergency events. Copyright © 2016 John Wiley & Sons, Ltd.

Software architectures and implementation technologies.- From Hubs Via Holons to an Adaptive Meta-Architecture - the AD-HOC Approach.- A C++ Workbench with Accurate Non-Blocking Garbage Collector for Server Side Internet Applications.- Scenario-based Component Behavior Filtration.- Mobile Ambients in Aspect-Oriented Software Architectures.- The architecture of distributed systems driven by autonomic patterns.- Modeling.- An optimizing OCL Compiler for Metamodeling and Model Transformation Environments.- Crossing the Borderline - From Formal to Semi-Formal Specifications.- Modeling of Component-Based Self-Adapting Context-Aware Applications for Mobile Devices.- A Performance Analysis Infrastructure for Component-Based System Hosted by Middleware.- Estimation of mean response time of multi-agent systems.- Integrated Approach to Modelling and Analysis using RTCP-nets.- Hybrid modeling and verification of Java based software.- Project management.- An evolutionary approach to project management process improvement for software-intensive projects.- Improved Bayesian Networks for Software Project Risk Assessment Using Dynamic Discretisation.- Software Risk Management: a Process Model and a Tool.- Software Quality.- An Approach to Software Quality Specification and Evaluation (SPoQE).- Feedback from Users on a Software Product to Improve Its Quality in Engineering Applications.- Reaching and Maintaining High Quality of Distributed J2EE Applications - BeesyCluster Case Study.- Verification methods.- Automatic software validation process.- j2eeprof - a tool for testing multitier applications.- An Analysis of Use Case Based Testing Approaches Based on a Defect Taxonomy.- Minimizing Test Execution Time During Test Generation.- An Integrated Regression Testing Framework to Multi-Threaded Java Programs.- DynAlloy as a Formal Method for the Analysis of Java Programs.- Verification of UML State Diagrams Using Concurrent State Machines.- Aspect-oriented Response Injection: an Alternative to Classical Mutation Testing.- Advanced mutation operators applicable in C# programs.- Data management.- An Open Platform of Data Quality Monitoring for ERP Information Systems.- Managing Data from Heterogeneous Data Sources Using Knowledge Layer.- Checkpoint-based resumption in data warehouses.- Software maintenance.- A C++ Refactoring Browser and Method Extraction.- ESC/Java2 as a Tool to Ensure Security in the Source Code of Java Applications.- Formalizing Software Refactoring in the Distributed Environment by aedNLC Graph Grammar.- Minik: A Tool for Maintaining Proper Java Code Structure.- Multidimensional Legacy Aspects of Modernizing Web Based Systems.

Are Forward Designed or Reverse-Engineered UML diagrams more helpful for code maintenance?: A family of experiments

Designed originally for document delivery, the Web is now being widely used as a platform for electronic commerce application software. The ad hoc enhancements that have made Web application software possible (for example, CGI and Java Script) have created an application support infrastructure where application software upgrades and maintenance are very complex. Yet the Web is the preferred platform for applications that have continuous ongoing development needs. In this chapter, we describe a model, an architecture, and an associated Web Application Support Environment (WASE) that both hides the low-level complexity of the existing Web infrastructure and, at the same time, empowers enterprise Web application programmers in their objective of writing modular and easily maintainable software applications for electronic commerce. WASE is not a compiler and does not completely abstract away the unique features of Web infrastructure. It is being constructed using XML documents in its API, to allow the function and configurability of applications to be defined in a Web-like fashion.

Typically, the identification and analysis of duplicate bug records of a software application are mundane activities, carried out by software maintenance engineers. As the bug repository grows in size for a large software application, this manual process becomes erroneous and a time-consuming activity. Automatic detection of these duplicate bug records will reduce the manual effort spent by the maintenance engineers. It also results in the reduction of costs of software maintenance. There are two types of duplicate bug records: (1) the records that describe the same problem using similar vocabulary, and (2) the records that describe different problems using dissimilar vocabulary but share the same underlying root cause. Each of these types of records needs a different set of techniques to identify the duplicate bug records. In this chapter, we explain the various machine learning techniques that are used to detect both types of duplicate bug records. Some of these duplicate bug records reappear, that is, they show up continuously over a long period of time. Here, we present a framework that can be used to automate the entire process of detection of both types of duplicates and recurring bug records. Using the framework, we conducted empirical studies on the open-source Chrome bug data records that are accessible online and the results are reported.

From the Publisher: What should be every software organization's primary goal? Managing Software Quality. Producing and sustaining the high quality of products and processes in evolutionary systems are at the core of software engineering, and it is only through a comprehensive measurement program that a successful outcome can be assured. Cost and budget limitations, schedule due dates, all represent systems engineering constraints which impinge on the degree which software development and maintenance professional can achieve maximum quality. Richard Nance and James Arthur's guide managing software quality goes beyond the usual answers the why and questions generally provided in the standards documents. They not only look at the how to in their focus of the measurement of software quality, but also come up with specific suggestions the pressing needs of practising software engineers, quality assurance engineers and software and project managers. is one of the few books in this area that addresses the 'quality' aspect based upon the important aspect of documentation. In addition, the book provides a basis for not only the software manager concerned with measurement implementation, but also the researcher in identifying the current state of the art and practice. This will be a key reference guide for anyone that is concerned with developing quality software. (William H Farr, PhD, Naval Surface Warfare Center Dahlgren Division) About the Authors: Research motivated by problems arising in large, complex software systems is what stimulates Richard Nance. His collaboration with the U.S. Navy on major software-intensive programs spans over 30 years. James Arthur is an AssociateProfessor of Computer Science at Virginia Tech.

Purpose The study aims to investigate the application of the data element market in software project management, focusing on improving effort estimation by addressing challenges faced by traditional methods. Design/methodology/approach This study proposes a solution based on feature selection, utilizing the data element market and reinforcement learning-based algorithms to enhance the accuracy of software effort estimation. It explores the application of the MARLFS algorithm, customizing improvements to the algorithm and reward function. Findings This study demonstrates that the proposed approach achieves more precise estimation compared to traditional methods, leveraging feature selection to guide project management in software development. Originality/value This study contributes to the field by offering a novel approach that com-bines the data element market, machine learning, and feature selection to improve software effort estimation, addressing limitations of traditional methods and providing insights for future research in project management.

Overview of Software Industry in Germany

Quantitative approaches to software schedule and cost estimation have been developed to help project managers to estimate schedule and cost for a software project and do some sensitivity analyses based on various criteria. Such models are useful and complement the manager's judgement and intuition for decision-making. Large deviations from the estimates can cause partial/total failure of a software project. Since risk and estimation go hand in hand, the project manager must identify and analyze risk factors to take corrective actions. In this paper a systematic approach to risk analysis is illustrated for a case study in addition a methodology for software effort estimation is proposed. Effort estimated for the case study from this method is compared with values obtained from other available methods in literature.