Estimation of Defect proneness Using Design complexity Measurements in Object- Oriented Software

Software engineering is continuously facing the challenges of growing complexity of software packages and increased level of data on defects and drawbacks from software production process. This makes a clarion call for inventions and methods which can enable a more reusable, reliable, easily maintainable and high quality software systems with deeper control on software generation process. Quality and productivity are indeed the two most important parameters for controlling any industrial process. Implementation of a successful control system requires some means of measurement. Software metrics play an important role in the management aspects of the software development process such as better planning, assessment of improvements, resource allocation and reduction of unpredictability. The process involving early detection of potential problems, productivity evaluation and evaluating external quality factors such as reusability, maintainability, defect proneness and complexity are of utmost importance. Here we discuss the application of CK metrics and estimation model to predict the external quality parameters for optimizing the design process and production process for desired levels of quality. Estimation of defect-proneness in object-oriented system at design level is developed using a novel methodology where models of relationship between CK metrics and defect-proneness index is achieved. A multifunctional estimation approach captures the correlation between CK metrics and defect proneness level of software modules.Comment: 5 pages, 1 figur

Software Metrics and Dashboard

Software metrics are a critical tool which provide continuous insight to products and processes and help build reliable software in mission critical environments. Using software metrics we can perform calculations that help assess the effectiveness of the underlying software or process. The two types of metrics relevant to our work is complexity metrics and in-process metrics. Complexity metrics tend to focus on intrinsic code properties like code complexity. In-process metrics focus on a higher-level view of software quality, measuring information that can provide insight into the underlying software development process. Our aim is to develop and evaluate a metrics dashboard to support Computational Science and Engineering (CSE) software development projects. This task requires us to perform the following activities: Assess how metrics are used and which general classes/types of metrics will be useful in CSE projects. Develop a metrics dashboard that will work for teams using sites like Github, Bitbucket etc. Assess the effectiveness of the dashboard in terms of project success and developer attitude towards metrics and process. Our current focus is on identifying requirements for the metrics dashboard which include the types of metrics that will help understand and improve the software quality. We have also started the development on the metrics dashboard based on the currently identified metrics types. We plan to provide a reliable metrics dashboard which could be used by the CSE development teams to improve their software quality, this will be done by instrumenting the metrics dashboard to gather usage statistics. In this way the dashboard evolves continuously

A Survey of Software Metric Use in Research Software Development

Background: Breakthroughs in research increasingly depend on complex software libraries, tools, and applications aimed at supporting specific science, engineering, business, or humanities disciplines. The complexity and criticality of this software motivate the need for ensuring quality and reliability. Software metrics are a key tool for assessing, measuring, and understanding software quality and reliability. Aims: The goal of this work is to better understand how research software developers use traditional software engineering concepts, like metrics, to support and evaluate both the software and the software development process. One key aspect of this goal is to identify how the set of metrics relevant to research software corresponds to the metrics commonly used in traditional software engineering. Method: We surveyed research software developers to gather information about their knowledge and use of code metrics and software process metrics. We also analyzed the influence of demographics (project size, development role, and development stage) on these metrics. Results: The survey results, from 129 respondents, indicate that respondents have a general knowledge of metrics. However, their knowledge of specific SE metrics is lacking, their use even more limited. The most used metrics relate to performance and testing. Even though code complexity often poses a significant challenge to research software development, respondents did not indicate much use of code metrics. Conclusions: Research software developers appear to be interested and see some value in software metrics but may be encountering roadblocks when trying to use them. Further study is needed to determine the extent to which these metrics could provide value in continuous process improvement

A Quality Model for Actionable Analytics in Rapid Software Development

Background: Accessing relevant data on the product, process, and usage perspectives of software as well as integrating and analyzing such data is crucial for getting reliable and timely actionable insights aimed at continuously managing software quality in Rapid Software Development (RSD). In this context, several software analytics tools have been developed in recent years. However, there is a lack of explainable software analytics that software practitioners trust. Aims: We aimed at creating a quality model (called Q-Rapids quality model) for actionable analytics in RSD, implementing it, and evaluating its understandability and relevance. Method: We performed workshops at four companies in order to determine relevant metrics as well as product and process factors. We also elicited how these metrics and factors are used and interpreted by practitioners when making decisions in RSD. We specified the Q-Rapids quality model by comparing and integrating the results of the four workshops. Then we implemented the Q-Rapids tool to support the usage of the Q-Rapids quality model as well as the gathering, integration, and analysis of the required data. Afterwards we installed the Q-Rapids tool in the four companies and performed semi-structured interviews with eight product owners to evaluate the understandability and relevance of the Q-Rapids quality model. Results: The participants of the evaluation perceived the metrics as well as the product and process factors of the Q-Rapids quality model as understandable. Also, they considered the Q-Rapids quality model relevant for identifying product and process deficiencies (e.g., blocking code situations). Conclusions: By means of heterogeneous data sources, the Q-Rapids quality model enables detecting problems that take more time to find manually and adds transparency among the perspectives of system, process, and usage.Comment: This is an Author's Accepted Manuscript of a paper to be published by IEEE in the 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018. The final authenticated version will be available onlin

Metrics Dashboard Services: A Framework for Analyzing Free/Open Source Team Repositories

Software engineering as practiced today (especially in the industry) is no longer about the stereotypical monolithic life cycle processes (e.g. waterfall, spiral, etc.) found in most software engineering textbooks. These heavyweight methods historically have impeded progress for small/medium sized development teams owing to their inherent complexity and rather limited data collection strategies that predominated the 1980s until relatively recently in the mid-2000s. The discipline and practice of software engineering includes software quality, which has an established theoretical foundation for doing software metrics. Software metrics are a critical tool which provide continuous insight to products and processes and help build reliable software in mission critical environments. Using software metrics we can perform calculations that help assess the effectiveness of the underlying software or process. The type of metrics relevant to our work are in-process metrics. In-process metrics focus on a higher-level view of software quality, measuring information that can provide insight into the underlying software development process. In this thesis, we aim to develop and evaluate a metrics dashboard to support Computational Science and Engineering (CSE) software development projects. This task requires us to perform the following activities: Assess how metrics are used and which general classes/types of metrics will be useful in CSE projects. Develop a metrics dashboard that will work for teams using sites like Github, Bitbucket etc. Assess the effectiveness of the dashboard in terms of project success and developer attitude towards metrics and process. The challenge is to cherry-pick the most-effective practices from a large suite of tools and incorporate them into existing cloud-based workflows. As part of this thesis, we have developed a metrics dashboard based on the currently identified metrics types. The tools we are developing can be incorporated in any existing GitHub-based workflow without requiring the developers to install anything