A Conceptual Investigation of Maintenance Deferral and Implementation: Foundation for a Maintenance Lifecycle Model

Despite the fact that society and organizations rely heavily on Information Systems (IS) and software, the maintenance of vendor-supplied IS, in particular standard package software has gained little attention within the academic literature. This paper presents a conceptual study of the current state of research concerning the reasons for deferral and performance of vendor-supplied maintenance by the purchasing organization. These reasons have so far neither been investigated together nor from that perspective. Based on a systematic literature review and taking the purchaser’s viewpoint, reasons for maintenance deferral and performance are identified from the literature. They build the groundwork and foundation for a Maintenance Lifecycle and Process Model that provides a starting point to research vendor-supplied maintenance from the customer’s point of view

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 conceptual model for unifying variability in space and time: Rationale, validation, and illustrative applications

With the increasing demand for customized systems and rapidly evolving technology, software engineering faces many challenges. A particular challenge is the development and maintenance of systems that are highly variable both in space (concurrent variations of the system at one point in time) and time (sequential variations of the system, due to its evolution). Recent research aims to address this challenge by managing variability in space and time simultaneously. However, this research originates from two different areas, software product line engineering and software configuration management, resulting in non-uniform terminologies and a varying understanding of concepts. These problems hamper the communication and understanding of involved concepts, as well as the development of techniques that unify variability in space and time. To tackle these problems, we performed an iterative, expert-driven analysis of existing tools from both research areas to derive a conceptual model that integrates and unifies concepts of both dimensions of variability. In this article, we first explain the construction process and present the resulting conceptual model. We validate the model and discuss its coverage and granularity with respect to established concepts of variability in space and time. Furthermore, we perform a formal concept analysis to discuss the commonalities and differences among the tools we considered. Finally, we show illustrative applications to explain how the conceptual model can be used in practice to derive conforming tools. The conceptual model unifies concepts and relations used in software product line engineering and software configuration management, provides a unified terminology and common ground for researchers and developers for comparing their works, clarifies communication, and prevents redundant developments

Software architecture knowledge for intelligent light maintenance

The maintenance management plays an important role in the monitoring of business activities. It ensures a certain level of services in industrial systems by improving the ability to function in accordance with prescribed procedures. This has a decisive impact on the performance of these systems in terms of operational efficiency, reliability and associated intervention costs. To support the maintenance processes of a wide range of industrial services, a knowledge-based component is useful to perform the intelligent monitoring. In this context we propose a generic model for supporting and generating industrial lights maintenance processes. The modeled intelligent approach involves information structuring and knowledge sharing in the industrial setting and the implementation of specialized maintenance management software in the target information system. As a first step we defined computerized procedures from the conceptual structure of industrial data to ensure their interoperability and effective use of information and communication technologies in the software dedicated to the management of maintenance (E-candela). The second step is the implementation of this software architecture with specification of business rules, especially by organizing taxonomical information of the lighting systems, and applying intelligencebased operations and analysis to capitalize knowledge from maintenance experiences. Finally, the third step is the deployment of the software with contextual adaptation of the user interface to allow the management of operations, editions of the balance sheets and real-time location obtained through geolocation data. In practice, these computational intelligence-based modes of reasoning involve an engineering framework that facilitates the continuous improvement of a comprehensive maintenance regime

An Empirical Investigation of the Key Factors for Refactoring Success in an Industrial Context

Refactoring is an increasingly practiced method in industrial software development. Stated simply, refactoring is an ongoing software improvement process that simplifies the internal structure of existing software, without changing its external behavior. The purpose is to improve the software and facilitate future maintenance and enhancement. Existing studies on refactoring mainly focus on its technical aspects and thus do not consider the team and human factors that influence its success. To identify the major facilitating factors for the success of refactoring, we interviewed 10 industrial software developers, and combined their responses with a study of the existing literature, formulated a model of refactoring success. The resulting conceptual model comprises both technical and non-technical factors. Technical factors include: level, testing and debugging, and tools, while the non-technical factors include: communication and coordination, support activities, individual capability/skills, and programmer participation. We propose to verify this model empirically through a survey of professional software developers (main body of refactoring practitioners). The survey design is provided

Critical success factors for DevOps adoption in information systems development

Adopting DevOps is challenging since it makes a significant paradigm shift in the Information Systems Development process. DevOps is a trending approach attached to the Agile Software Development Methodology, which facilitates adaptation to the customers' rapidly-changing requirements. It keeps one front step by introducing software operators who support the transmission between software and implementation into the software development team by confirming faster development, quality assurance, and easy maintenance of Information Systems (IS). However, software development companies reported challenges in adopting DevOps. It is critical to control those challenges while getting hold of the benefits by studying Critical Success Factors (CSF) for adopting DevOps. This study aimed to analyze the use of DevOps approach in IS developments by exploring CSFs of DevOps. A systematic literature review was applied to identify CSFs. These factors were confirmed by interviewing DevOps practitioners while identifying more frequent CSFs in the software development industry. Finally, the research presents a conceptual model for CSFs of DevOps, which is a guide to reap the DevOps benefits while reducing the hurdles for enhancing the success of IS. The conceptual model presents CSFs of DevOps by grouping them into four areas: collaborative culture, DevOps practices, proficient DevOps team, and metrics & measurement

Critical success factors for DevOps adoption in information systems development

Adopting DevOps is challenging since it makes a significant paradigm shift in the Information Systems Development process. DevOps is a trending approach attached to the Agile Software Development Methodology, which facilitates adaptation to the customers\u27 rapidly-changing requirements. It keeps one front step by introducing software operators who support the transmission between software and implementation into the software development team by confirming faster development, quality assurance, and easy maintenance of Information Systems. However, software development companies reported challenges in adopting DevOps. It is critical to control those challenges while getting hold of the benefits by studying Critical Success Factors (CSF) for adopting DevOps. This study aimed to analyze the use of DevOps approach in IS developments by exploring CSFs of DevOps. A systematic literature review was applied to identify CSFs. These factors were confirmed by interviewing DevOps practitioners while identifying more frequent CSFs in the software development industry. Finally, the research presents a conceptual model for CSFs of DevOps, which is a guide to reap the DevOps benefits while reducing the hurdles for enhancing the success of Information Systems. The conceptual model presents CSFs of DevOps by grouping them into four areas: collaborative culture, DevOps practices, proficient DevOps team, and Metrics & Measurement

Constructive Reasoning for Semantic Wikis

One of the main design goals of social software, such as wikis, is to support and facilitate interaction and collaboration. This dissertation explores challenges that arise from extending social software with advanced facilities such as reasoning and semantic annotations and presents tools in form of a conceptual model, structured tags, a rule language, and a set of novel forward chaining and reason maintenance methods for processing such rules that help to overcome the challenges. Wikis and semantic wikis were usually developed in an ad-hoc manner, without much thought about the underlying concepts. A conceptual model suitable for a semantic wiki that takes advanced features such as annotations and reasoning into account is proposed. Moreover, so called structured tags are proposed as a semi-formal knowledge representation step between informal and formal annotations. The focus of rule languages for the Semantic Web has been predominantly on expert users and on the interplay of rule languages and ontologies. KWRL, the KiWi Rule Language, is proposed as a rule language for a semantic wiki that is easily understandable for users as it is aware of the conceptual model of a wiki and as it is inconsistency-tolerant, and that can be efficiently evaluated as it builds upon Datalog concepts. The requirement for fast response times of interactive software translates in our work to bottom-up evaluation (materialization) of rules (views) ahead of time – that is when rules or data change, not when they are queried. Materialized views have to be updated when data or rules change. While incremental view maintenance was intensively studied in the past and literature on the subject is abundant, the existing methods have surprisingly many disadvantages – they do not provide all information desirable for explanation of derived information, they require evaluation of possibly substantially larger Datalog programs with negation, they recompute the whole extension of a predicate even if only a small part of it is affected by a change, they require adaptation for handling general rule changes. A particular contribution of this dissertation consists in a set of forward chaining and reason maintenance methods with a simple declarative description that are efficient and derive and maintain information necessary for reason maintenance and explanation. The reasoning methods and most of the reason maintenance methods are described in terms of a set of extended immediate consequence operators the properties of which are proven in the classical logical programming framework. In contrast to existing methods, the reason maintenance methods in this dissertation work by evaluating the original Datalog program – they do not introduce negation if it is not present in the input program – and only the affected part of a predicate’s extension is recomputed. Moreover, our methods directly handle changes in both data and rules; a rule change does not need to be handled as a special case. A framework of support graphs, a data structure inspired by justification graphs of classical reason maintenance, is proposed. Support graphs enable a unified description and a formal comparison of the various reasoning and reason maintenance methods and define a notion of a derivation such that the number of derivations of an atom is always finite even in the recursive Datalog case. A practical approach to implementing reasoning, reason maintenance, and explanation in the KiWi semantic platform is also investigated. It is shown how an implementation may benefit from using a graph database instead of or along with a relational database

Generic modelling of code clones

Code clones, i.e. instances of duplicated code, can be found in many software systems. They adversely affect the software systems ’ quality, in particular their maintainability and comprehensibility. Thus, this as-pect is particularly important to consider in software maintenance and re-engineering. Many different algorithms detecting code clones have been developed. For various reasons, it is difficult to compare the results of different algorithms. Most notable among these reasons is that there is no conceptual model allowing description of code clones determined by different algorithms. Much more, each algorithm uses its specific concept of code clones, which is rarely made explicit. To overcome these problems, we have developed a generic model for describing clones. The model is generic in that it is independent of the pro-gramming language examined and of the clone detection algorithm used. It is flexible enough to facilitate various granularities of artifacts employed for selection and comparison, including inexact clones. The model allows separation of concerns between clone detection, description and manage-ment, which reduces the effort for the implementation of tools supporting these activities. On the basis of the model, we have implemented a pro-totype tool supporting these activities, tightly integrated into the Eclipse environment.