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

Evaluation of the Design Metric to Reduce the Number of Defects in Software Development

Software design is one of the most important and key activities in the system development life cycle (SDLC) phase that ensures the quality of software. Different key areas of design are very vital to be taken into consideration while designing software. Software design describes how the software system is decomposed and managed in smaller components. Object-oriented (OO) paradigm has facilitated software industry with more reliable and manageable software and its design. The quality of the software design can be measured through different metrics such as Chidamber and Kemerer (CK) design metrics, Mood Metrics & Lorenz and Kidd metrics. CK metrics is one of the oldest and most reliable metrics among all metrics available to software industry to evaluate OO design. This paper presents an evaluation of CK metrics to propose an improved CK design metrics values to reduce the defects during software design phase in software. This paper will also describe that whether a significant effect of any CK design metrics exists on total number of defects per module or not. This is achieved by conducting survey in two software development companies.Comment: 9 Page

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

Generalization Index: Defining a metric for the detection of smells in UML Class Diagrams in Eclipse Modeling Framework in Eclipse

In the field of Software Engineering, while designing the software design and maintaining the source code quality, a lot of good and bad practices come into being. With the continuous evolutions in the field of modeling in software development processes, Model Driven Software Development (MDSD), focuses towards the quality of software models. Unified Modeling Language (UML) is a graphical notation for expressing object-oriented designs. With its emergence as a modeling standard and being widely accepted by most software development organizations, in this research paper we focus on UML Class Diagrams. Metrics are mathematical models used for measuring. In software engineering, metrics are utilized for measuring quality aspects of software models. A manual model review is very time consuming and prone to errors, so it becomes essential to automate the tasks as effectively as possible. The Eclipse plug-in EMF Metrics supports specification and calculation of metrics wrt. specific EMF based models. A new definition technique for EMF quality assurance can be defined using Java, an OCL query or Henshin Pattern. In this paper we propose an algorithm for the calculation a new metric named Generalization Index Metric (GIX) for Java Code