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"Software maintenance"
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Challenges in Agile Software Maintenance for Local and Global Development: An Empirical Assessment
Agile methods have gained wide popularity recently due to their characteristics in software development. Despite the success of agile methods in the software maintenance process, several challenges have been reported. In this study, we investigate the challenges that measure the impact of agile methods in software maintenance in terms of quality factors. A survey was conducted to collect data from agile practitioners to establish their opinions about existing challenges. As a result of the statistical analysis of the data from the survey, it has been observed that there are moderately effective challenges in manageability, scalability, communication, collaboration, and transparency. Further research is required to validate software maintenance challenges in agile methods.
Journal Article
Kubernetes : up and running : dive into the future of infrastructure
\"Kubernetes is here to stay. In just five years, this container orchestrator has radically changed the way developers and ops personnel build, deploy, and maintain applications in the cloud. The updated edition of this popular book explains how Kubernetes can help your company achieve new levels of velocity, agility, reliability, and efficiency-- whether you're new to distributed systems or have been deploying cloud native apps for some time\"-- Provided by publisher.
Book
Industrial adoption of machine learning techniques for early identification of invalid bug reports
Despite the accuracy of machine learning (ML) techniques in predicting invalid bug reports, as shown in earlier research, and the importance of early identification of invalid bug reports in software maintenance, the adoption of ML techniques for this task in industrial practice is yet to be investigated. In this study, we used a technology transfer model to guide the adoption of an ML technique at a company for the early identification of invalid bug reports. In the process, we also identify necessary conditions for adopting such techniques in practice. We followed a case study research approach with various design and analysis iterations for technology transfer activities. We collected data from bug repositories, through focus groups, a questionnaire, and a presentation and feedback session with an expert. As expected, we found that an ML technique can identify invalid bug reports with acceptable accuracy at an early stage. However, the technique’s accuracy drops over time in its operational use due to changes in the product, the used technologies, or the development organization. Such changes may require retraining the ML model. During validation, practitioners highlighted the need to understand the ML technique’s predictions to trust the predictions. We found that a visual (using a state-of-the-art ML interpretation framework) and descriptive explanation of the prediction increases the trustability of the technique compared to just presenting the results of the validity predictions. We conclude that trustability, integration with the existing toolchain, and maintaining the techniques’ accuracy over time are critical for increasing the likelihood of adoption.
Journal Article
Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes
Empirical validation of software metrics suites to predict fault proneness in object-oriented (OO) components is essential to ensure their practical use in industrial settings. In this paper, we empirically validate three OO metrics suites for their ability to predict software quality in terms of fault-proneness: the Chidamber and Kemerer (CK) metrics, Abreu's Metrics for Object-Oriented Design (MOOD), and Bansiya and Davis' Quality Metrics for Object-Oriented Design (QMOOD). Some CK class metrics have previously been shown to be good predictors of initial OO software quality. However, the other two suites have not been heavily validated except by their original proposers. Here, we explore the ability of these three metrics suites to predict fault-prone classes using defect data for six versions of Rhino, an open-source implementation of JavaScript written in Java. We conclude that the CK and QMOOD suites contain similar components and produce statistical models that are effective in detecting error-prone classes. We also conclude that the class components in the MOOD metrics suite are not good class fault-proneness predictors. Analyzing multivariate binary logistic regression models across six Rhino versions indicates these models may be useful in assessing quality in OO classes produced using modern highly iterative or agile software development processes.
Journal Article
Predicting maintenance performance using object-oriented design complexity metrics
The Object-Oriented (OO) paradigm has become increasingly popular in recent years. Researchers agree that, although maintenance may turn out to be easier for OO systems, it is unlikely that the maintenance burden will completely disappear. One approach to controlling software maintenance costs is the utilization of software metrics during the development phase, to help identify potential problem areas. Many new metrics have been proposed for OO systems, but only a few of them have been validated. The purpose of this research is to empirically explore the validation of three existing OO design complexity metrics and, specifically, to assess their ability to predict maintenance time. This research reports the results of validating three metrics, Interaction Level (IL), Interface Size (IS), and Operation Argument Complexity (OAC). A controlled experiment was conducted to investigate the effect of design complexity (as measured by the above metrics) on maintenance time. Each of the three metrics by itself was found to be useful in the experiment in predicting maintenance performance.
Journal Article
On the relationship of class stability and maintainability
Maintainability is an essential software quality attribute as software maintenance is a costly process. ISO 9126 characterised maintainability with five sub-characteristics, one of which is stability. Unstable software may lead to high maintenance cost and effort. Classes in object-oriented systems form the basic elements of the software architecture; hence, stable classes may contribute to reducing the software maintenance cost and effort. In this study, the author conducts an empirical study to evaluate the relationship between class stability and maintainability. The author correlates class stability with maintainability effort measured by the number of hours spent on maintenance activities and by the line of code changes. Results show that classes with higher values of stability measured by the class stability metric (CSM) are associated with a lower value of perfective maintenance effort measured by hours. CSM also correlated with all types of maintenance (corrective, adaptive and perfective) if measured for the cumulatively combined system classes in all iterations rather than per iteration. The author also found that none of the stability metrics show a relationship with maintainability when measured by number of line of code changes.
Journal Article
Comparing uniform and flexible policies for software maintenance and replacement
The importance of software maintenance in managing the life-cycle costs of a system cannot be overemphasized. Beyond a point, however, it is better to replace a system rather than maintain it. We derive model and operating policy that reduces the sum of maintenance and replacement costs in the useful life of a software system. The main goal is to compare uniform (occurring at fixed time intervals) versus flexible (occurring at varying, planned time intervals) polices for maintenance and replacement. The model draws from the empirical works of earlier researchers to consider 1) inclusion of user requests for maintenance, 2) scale economies in software maintenance, 3) efficiencies derived from replacing old software technology with new software technology, and 4) the impact of software reuse on replacement and maintenance. Results from our model show that the traditional practice of maintaining or replacing a software system at uniform time intervals may not be optimal. We also find that an increase in software reuse leads to more frequent replacement, but the number of maintenance activities is not significantly impacted.
Journal Article
Application Administrators Handbook
This book provides an overview of every phase of administering a software application, from working with the vendor before installation, the installation process itself, importing data into the application, handling upgrades, optimizing applications, debuging applications and working with application users to report problems, scheduling backups, automating tasks that need to be done on a repetitive schedule, and finally retiring an application. It provides detailed, hands-on instructions on how to perform these tasks, including: installing, administering and maintaining key software applications throughout the product life cycle; steps that should be taken before installing or upgrading an application to ensure continuous operation; identifying repetitive tasks and finding out how they can be automated, thereby saving valuable time; and understanding the latest on government mandates and regulations, such as privacy, SOX, HIPAA, PCI, and FISMA and how to fully comply. --
eBook
Working with Legacy Systems
The IT industry is obsessed with new technologies. Courses, books, and magazines mostly focus on what is new. Starting with what a legacy system looks like to applying various techniques for maintaining and securing these systems, this book gives you all the knowledge you need to maintain a legacy system.
eBook