Code Quality Analysis Research Articles

A Cloud-KPIs Dashboard to Evaluate Agile Development Teams' Performance

In agile software development, the performance of human development teams is pivotal for project success, yet assessing their performance can be challenging, given the flexibility of agile project management. This article introduces predictive metrics for evaluating development teams, including indicators like delivery speed, code quality, internal collaboration, and problem-solving efficiency. It also discusses the tools and techniques available for measuring team performance, such as productivity tracking, code quality analysis, and feedback from users and customers. Real-world experiences with these metrics demonstrate their effectiveness in significantly enhancing human development teams' efficiency and effectiveness in managing agile software projects. By utilizing these metrics, development teams can pinpoint areas for improvement, adapt to changes, and enhance their capacity and skills to meet project objectives. In conclusion, this article underscores how predictive metrics contribute to the success of development teams in agile software projects within an ever-changing.

Applying static code analysis for domain-specific languages

The use of code quality control platforms for analysing source code is increasingly gaining attention in the developer community. These platforms are prepared to parse and check source code written in a variety of general-purpose programming languages. The emergence of domain-specific languages enables professionals from different areas to develop and describe problem solutions in their disciplines. Thus, source code quality analysis methods and tools can also be applied to software artefacts developed with a domain-specific language. To evaluate the quality of domain-specific language code, every software component required by the quality platform to parse and query the source code must be developed. This becomes a time-consuming and error-prone task, for which this paper describes a model-driven interoperability strategy that bridges the gap between the grammar formats of source code quality parsers and domain-specific text languages. This approach has been tested on the most widespread platforms for designing text-based languages and source code analysis. This interoperability approach has been evaluated on a number of specific contexts in different domain areas.

Implementation of docker integrated with cloud computing and code quality analysis (test automation)

The computer science industry is projected to grow much faster than other industries over the next 10 years focusing on delivering the best quality of soft wares and application to client within a short period of time using Agile Methodology this method anticipates change and allows for much more flexibility than traditional methods of development of software. The process involves breaking down each project into prioritized requirements, and delivering each individually within an iterative cycle. Iteration is the routine of developing small sections of a project at a time. Each iteration is reviewed and assessed by the development team and client. Quality of the code is essential for agile development environment as there can be frequent changes in requirements which lead to frequent code changes. Therefore the source code should be written in a way that it can be maintained and extended easily. In order to achieve this, developers should be thoroughly concerned about the coding standards and best practices. SonarQube can be highly beneficial for developers to ensure that their code is in high quality. Whereas Docker is an open-source technology that allows you to build, run, test, and deploy distributed applications inside software containers. It allows you to package a piece of software in a standardized unit for software development, containing everything the software needs to run: code, runtime, system tools, system libraries, etc. Docker enables you to quickly, reliably, and consistently deploy applications regardless of environment. Running Docker on AWS provides a highly reliable, low-cost way to quickly build, run, test, and deploy distributed applications at any scale. AWS provides support for Docker open-source and commercial solutions within AWS services

Surfing the net for software engineering notes

What is good code? A simple question, but how can you tell if a load o' code is good stuff or a pile of spaghetti? That's where source code quality analysis comes into play. In this month's column, we're going to visit some web sites that will help you establish or improve your code quality processes.

Code quality analysis in open source software development

Abstract Proponents of open source style software development claim that better software is produced using this model compared with the traditional closed model. However, there is little empirical evidence in support of these claims. In this paper, we present the results of a pilot case study aiming: (a) to understand the implications of structural quality; and (b) to figure out the benefits of structural quality analysis of the code delivered by open source style development. To this end, we have measured quality characteristics of 100 applications written for Linux, using a software measurement tool, and compared the results with the industrial standard that is proposed by the tool. Another target of this case study was to investigate the issue of modularity in open source as this characteristic is being considered crucial by the proponents of open source for this type of software development. We have empirically assessed the relationship between the size of the application components and the delivered quality measured through user satisfaction. We have determined that, up to a certain extent, the average component size of an application is negatively related to the user satisfaction for this application.

Guest Editorial Open source software: investigating the software engineering, psychosocial and economic issues

Guest Editorial Open source software: investigating the software engineering, psychosocial and economic issues