Predicting Defects in Software Using Grammar-Guided Genetic Programming

The knowledge of the software quality can allow an organization to allocate the needed resources for the code maintenance. Maintaining the software is considered as a high cost factor for most organizations. Consequently, there is need to assess software modules in respect of defects that will arise. Addressing the prediction of software defects by means of computational intelligence has only recently become evident. In this paper, we investigate the capability of the genetic programming approach for producing solution composed of decision rules. We applied the model into four software engineering databases of NASA. The overall performance of this system denotes its competitiveness as compared with past methodologies, and is shown capable of producing simple, highly accurate, tangible rules

Adaptive decision support for suggesting a machine tool maintenance strategy: from reactive to preventative

Purpose -- To produce a decision support aid for machine tool owners to utilise while deciding upon a maintenance strategy. Furthermore, the decision support tool is adaptive and capable of suggesting different strategies by monitoring for any change in machine tool manufacturing accuracy. Design/methodology/approach -- A maintenance cost estimation model is utilised within the research and development of this decision support system. An empirical-based methodology is pursued and validated through case study analysis. Findings -- A case study is provided where a schedule of preventative maintenance actions is produced to reduce the need for the future occurrences of reactive maintenance actions based on historical machine tool accuracy information. In the case-study, a 28% reduction in predicted accuracy-related expenditure is presented, equating to a saving of £14k per machine over a five year period. Research limitations/implications -- The emphasis on improving machine tool accuracy and reducing production costs is increasing. The presented research is pioneering in the development of a software-based tool to help reduce the requirement on domain-specific expert knowledge. Originality/value -- The paper presents an adaptive decision support system to assist with maintenance strategy selection. This is the first of its kind and is able to suggest a preventative strategy for those undertaking only reactive maintenance. This is of value for both manufacturers and researchers alike. Manufacturers will benefit from reducing maintenance costs, and researchers will benefit from the development and application of a novel decision support technique

Adapting the SHEL model in investigating industrial maintenance

Purpose – The purpose of this paper is to identify and categorize problems in knowledge management of industrial maintenance, and support successful maintenance through adapting the SHEL model. The SHEL model has been used widely in airplane accident investigations and in aviation maintenance, but not in industrial maintenance. Design/methodology/approach – The data was collected by two separate surveys with open-ended questions from maintenance customers and service providers in Finland. The collected data was coded according to SHEL model -derived themes and analysed thematically with NVivo. Findings – The authors found that the adapted SHELO model works well in the industrial maintenance context. The results show that the most important knowledge management problems in the area are caused by interactions between Liveware and Software (information unavailability), Liveware and Liveware (information sharing), Liveware and Organisation (communication), and Software and Software (information integrity). Research limitations/implications – The data was collected only from Finnish companies and from the perspective of knowledge management. In practice there are also other kinds of issues in industrial maintenance. This can be a topic for future research. Practical implications – The paper presents a new systematic method to analyse and sort knowledge management problems in industrial maintenance. Both maintenance service customers and suppliers can improve their maintenance processes by using the dimensions of the SHELO model. Originality/value – The SHEL model has not been used in industrial maintenance before. In addition, the new SHELO model takes also interactions without direct human influence into account. Previous research has listed conditions for successful maintenance extensively, but this kind of prioritization tools are needed to support decision making in practice

A collaborative learning experience in modeling the requirements of teleoperated system for ship hull maintenance

This paper presents a join experience in modelling the requirements of the product line of teleoperated systems for ship hull maintenance, which are basically robotic systems used for ship maintenance operations, such as cleaning or painting the ship hull. It is proposed to specify the product line requirements through a feature model, a conceptual model, and a use case model, which together allow domain understanding, derivation of reusable product line requirements, and efficient decision-making in the specification of new systems developed in the product line. Action Research, a qualitative research method in software engineering, has been applied to define the collaborative research process

A framework of classifying maintenance requests based on learning techniques

Classify maintenance request is one of the processes in the large software system to support maintainers in doing their daily maintenance tasks more effectively. Categorizing these maintenance requests are an essential requirement in managing the maintenance request for software maintainer and need a great effort as well as determining classification. Hence, this paper presents the framework from the use of three different classification approaches, namely Bayesian model Decision Tree and Logistic regression. We show that naïve Bayesian classifier, Decision Tree and Logistic regression can be used to accurately classify issues into maintenance type

RANCANG BANGUN SISTEM INFORMASI PREVENTIVE MAINTENANCE BERBASIS WEB PADA PERUSAHAAN MANUFAKTUR

In order to improve the best service for its customers, PT Multi Engineering Perkasa sends a maintenance team and implements a preventive maintenance system on each machine distributed to their customers during the warranty period. Problems faced by maintenance such as delays in determining production machine repair schedules, machine repair job checklist data collection, and insufficient information facilities needed by PT Multi Engineering Perkasa regarding history of damage or checking on production machines which are still done manually, using hardcopy. This research uses the Waterfall model of Software Development Life Cycle (SDLC) development method. The waterfall model has five stages, namely analysis, design, coding, testing and maintenance. This application is created using the programming language PHP, HTML and MySQL as a database. And as a result, the web-based preventive maintenance information system can help maintenance in carrying out preventive maintenance data, especially helping in terms of information so that it can be used as a decision-making material, as well as on the part of the customer can quickly see the repair / preventive maintenance report data when needed

Software Process Management: A Model- Based Approach

Business processes constitute one major asset in an organization and software businesses are not an exception. Processes defi nition, maintenance, and management are key aspects to control and defi ne how to build software systems up and also to support decision-making. In this paper, a model-based approach is proposed to facilitate these processes. Thus, a global environment for business processes in software development is presented. The fi nal results are illustrated through the NDTQ-Framework, a solution based on this approach that is currently being used in software development organizations.Ministerio de Ciencia e Innovación TIN2010-20057-C03-02Ministerio de Ciencia e Innovación TIN 2010-12312-EJunta de Andalucía TIC-578

Academic Cloud ERP Quality Assessment Model

In the past few decades, educational institutions have been using conventional academic ERP system to integrate and optimize their business process. In this delivery model, each educational institutions are responsible of their own data, installation, and also maintenance. For some institutions, it might cause not only waste of resources, but also problems in management and financial aspects. Cloud-based Academic ERP, a SaaS-based ERP system, begin to come as a solution with is virtualization technology. It allows institutions to use only the needed ERP resources, without any specific installation, integration, or maintenance needs. As the implementation of Cloud ERP increases, problems arise on how to evaluate this system. Current evaluation approaches are either only evaluating the cloud computing aspects or only evaluating the software quality aspects. This paper proposes an assessment model for Cloud ERP system, considering both software quality characteristics and cloud computing attributes to help strategic decision makers evaluate academic Cloud ERP system

A Hyper-parameter Tuning based Novel Model for Prediction of Software Maintainability

Software maintainability is regarded as one of the most important characteristics of any software system. In today's digital world, the expanding significance of software maintenance is motivating the development of efficient software maintainability prediction (SMP) models using statistical and machine learning methods. This study proposes a hyper-parameter optimizable Software Maintainability Prediction (HPOSMP) model using the hybridized approach of data balancing and hyper-parameter optimization of Machine Learning (ML) approach using software maintainability datasets. The training dataset has been created with object-oriented software namely UIMS and QUES. To balance the dataset, Synthetic Minority Oversampling Technique (SMOTE) technology has been adopted. Further, Decision Tree, Gaussian Naïve Bayes, K-Nearest neighbour, Logistic Regression, and Support Vector Machine are adopted as Machine Learning and Statistical Regression Techniques for training of software maintainability dataset. Results demonstrate that the proposed HPOSMP model gives better performance as compared to the base SMP models