Many of software engineering tools and systems are focused to monitoring source code quality and optimizing software development. Many of them use similar source code metrics to solve different kinds of problems. This inspired us to propose an environment for platform independent code monitoring, which supports employment of multiple software development monitoring tools and sharing of information among them to reduce redundant calculations. In this paper we present design of an architecture of the environment, whose main contribution is employing (acquiring, generating and processing) information tags - descriptive metadata that indirectly refer source code artifacts, project documentations and developers activity via document models and user models. Information tags represent novel concept unifying traditional content based software metrics with recently developed activity-based metrics. We also describe prototype realization of the environment within project PerConIK (Personalized Conveying Information and Knowledge), which proves feasibility and usability of the proposed environment.

/pdf/platform-independent-software-development-monitoring-design-3yjfw14exb.pdf

Platform Independent Software Development Monitoring: Design of an Architecture

The role of software has changed drastically in recent years, it is found in practically all areas of the industry and has become an important investment factor for the companies, so that 4 of the top 5 companies with most capital are dedicated to software development. At the same time that software has grown and diversified, quality assurance activities have become increasingly important and complex. However, The Chaos Report, which evaluates the percentage of success and failure in software projects, mentions that there has been no improvement in the results obtained in recent years, where the average of successful projects is 37%. Considering the importance of quality assurance activities in software development, this paper proposes a tool that supports the implementation of practices for quality assurance, promoting and facilitating their implementation. The paper includes the description of the tool development, as well as the validations done to the tool.

Support tool for software quality assurance in software development

Siemens, Corporate Technology, Development Center, Asia Australia (CT DC AA) has been developing and maintaining several software projects for the Industry, Energy, Healthcare, and Infrastructure & Cities sectors of Siemens. The critical nature of these projects necessitates a high level of software code quality. As part of the code quality program at CT DC AA the strategy is to have a scalable method towards identification of issues affecting code quality of projects across the organization. Traditionally, code quality experts in Siemens used EMISQ method to assess code quality. EMISQ requires about three person months (two experts for six weeks) for 50-100 kLoC, making it effort intensive and time consuming. Thus, scaling this assessment method to include the hundreds of projects in CT DC AA poses many challenges. To address this, we have developed a lightweight assessment method called SCQAM (Structured Code Quality Assessment Method). SCQAM is an expert-based method wherein manual assessment of code quality by experts is directed by the systematic application of code analysis tools. In this paper, we describe the SCQAM method, experiences in applying it to projects in CT DC AA, challenges faced and initiatives taken to enable fixing of systemic issues reported by assessments. The insights from our SCQAM experience can provide useful pointers to other organizations and practitioners interested in assessment and improvement of software code quality.

SCQAM: a scalable structured code quality assessment method for industrial software

The quality of execution tracing impacts the time to a great extent to locate errors in software components; moreover, execution tracing is the most suitable tool, in the majority of the cases, for doing postmortem analysis of failures in the field. Nevertheless, software product quality models do not adequately consider execution tracing quality at present neither do they define the quality properties of this important entity in an acceptable manner. Defining these quality properties would be the first step towards creating a quality model for execution tracing. The current research fills this gap by identifying and defining the variables, i.e., the quality properties, on the basis of which the quality of execution tracing can be judged. The present study analyses the experiences of software professionals in focus groups at multinational companies, and also scrutinises the literature to elicit the mentioned quality properties. Moreover, the present study also contributes to knowledge with the combination of methods while computing the saturation point for determining the number of the necessary focus groups. Furthermore, to pay special attention to validity, in addition to the the indicators of qualitative research: credibility, transferability, dependability, and confirmability, the authors also considered content, construct, internal and external validity.

https://www.mdpi.com/2571-5577/4/1/20/pdf

Quality Properties of Execution Tracing, an Empirical Study

Program analysis static or dynamic is an important method to improve and maintain code quality. Static analysis gives immediate feedback during the development of a computer program, and can be a great help for programmers. The language design of the Ei el programming language already encourages high code quality, but the Ei el IDE has no light-weight static analysis tool. We designed a versatile and extensible rule-based framework for light-weight static program analysis for the Ei el language called Inspector Eiffel. We implemented a basic set of rules and developed a user interface that is integrated in Ei elStudio, the main Ei el IDE. The tool is fully usable by programmers and integrates well in the development process. We present several case studies of applying the tool on existing libraries and programs, which show the usefulness of the tool by detecting coding issues and suggesting improvements.

Rule-based code analysis

CQMM (Code Quality Monitoring Method) is a means for systematically monitoring and improving code level quality of a system during development. It employs goal directed monitoring using quality models and static code analysis tools. In this paper, we present the CQMM method, learnings gathered through pilot studies, and changes needed for its large scale adoption within our organization. This exercise was an important step towards evolving an organization wide common minimum baseline for code-centric quality. Initial results indicate that the process helps in exposing important code-centric issues, besides sensitizing developers to coding practices. We also demonstrate the usefulness of the approach by tracking code level issues on select open source projects.

Continual monitoring of code quality

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Continual monitoring of code quality