Today, small- and medium-sized enterprises (SMEs) in the software industry face major challenges. Their resource constraints require high efficiency in development. Furthermore, quality assurance (QA) measures need to be taken to mitigate the risk of additional, expensive effort for bug fixes or compensations. Automated static analysis (ASA) can reduce this risk because it promises low application effort. SMEs seem to take little advantage of this opportunity. Instead, they still mainly rely on the dynamic analysis approach of software testing. In this article, we report on our experiences from a technology transfer project. Our aim was to evaluate the results static analysis can provide for SMEs as well as the problems that occur when introducing and using static analysis in SMEs. We analysed five software projects from five collaborating SMEs using three different ASA techniques: code clone detection, bug pattern detection and architecture conformance analysis. Following the analysis, we applied a quality model to aggregate and evaluate the results. Our study shows that the effort required to introduce ASA techniques in SMEs is small (mostly below one person-hour each). Furthermore, we encountered only few technical problems. By means of the analyses, we could detect multiple defects in production code. The participating companies perceived the analysis results to be a helpful addition to their current QA and will include the analyses in their QA process. With the help of the Quamoco quality model, we could efficiently aggregate and rate static analysis results. However, we also encountered a partial mismatch with the opinions of the SMEs. We conclude that ASA and quality models can be a valuable and affordable addition to the QA process of SMEs.
@cite_17 reported on a survey with SMEs and their usage of quality standards. They found that quality is often not a prime objective in smaller companies and especially the micro-companies do not even know the standards.
@cite_17
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Under the studies of general core activities including software inspection, review and testing to achieve quality objectives in small-medium size enterprises (SMEs), the paper presents a contemporary view of such companies against quality measures. The results from a local empirical investigation of quality standards in the Turkish software industry are reported. Around 150 software companies have been approached from which 17 detailed feedback inform that in order to ensure software quality, standards including internationally recognized International Standards Organization (ISO) and Capability Maturity Model Integration (CMMI) are given credit. However the substantial workload and resources required to obtain them are also reported as serious; downscaled frameworks of such large models proposed in the literature are not well known by the SMEs either. The paper also discusses "work around" that bypasses such standards to ease delivery of products while keeping certificates as labels just to acquire new jobs for the business. Many companies struggle with unexpectedly high maintenance costs for their software development which are often caused by insufficient code quality. Although companies often use static analyses tools, they do not derive consequences from the metric results and, hence, the code quality does not actually improve. We provide an experience report of the quality consulting company CQSE, and show how code quality can be improved in practice: we revise our former expectations on quality control from [1] and propose an enhanced continuous quality control process which requires the combination of metrics, manual action, and a close cooperation between quality engineers, developers, and managers. We show the applicability of our approach with a case study on 41 systems of Munich RE and demonstrate its impact. Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework. The key premise of an organization is to allow more efficient production, including production of high quality software. To achieve that, an organization defines roles and reporting relationships. Therefore, changes in organization's structure are likely to affect product's quality. We propose and investigate a relationship between developer-centric measures of organizational change and the probability of customer-reported defects in the context of a large software project. We find that the proximity to an organizational change is significantly associated with reductions in software quality. We also replicate results of several prior studies of software quality supporting findings that code, change, and developer characteristics affect fault-proneness. In contrast to prior studies we find that distributed development decreases quality. Furthermore, recent departures from an organization were associated with increased probability of customer-reported defects, thus demonstrating that in the observed context the organizational change reduces product quality.