Abstract-Published software quality models either provide abstract quality attributes or concrete quality assessments. There are no models that seamlessly integrate both aspects. In the project Quamoco, we built a comprehensive approach with the aim to close this gap.For this, we developed in several iterations a meta quality model specifying general concepts, a quality base model covering the most important quality factors and a quality assessment approach. The meta model introduces the new concept of a product factor, which bridges the gap between concrete measurements and abstract quality aspects. Product factors have measures and instruments to operationalise quality by measurements from manual inspection and tool analysis. The base model uses the ISO 25010 quality attributes, which we refine by 200 factors and 600 measures for Java and C# systems.We found in several empirical validations that the assessment results fit to the expectations of experts for the corresponding systems. The empirical analyses also showed that several of the correlations are statistically significant and that the maintainability part of the base model has the highest correlation, which fits to the fact that this part is the most comprehensive. Although we still see room for extending and improving the base model, it shows a high correspondence with expert opinions and hence is able to form the basis for repeatable and understandable quality assessments in practice.

Most of today's products and services are software-based. Organizations that develop software want to maintain and improve their competitiveness by controlling software-related risks. To do this, they need to align their business goals with software development strategies and translate them into quantitative project management. There is also an increasing need to justify cost and resources for software and system development and other IT services by demonstrating their impact on an organization's higher-level goals. For both, linking business goals and software-related efforts in an organization is necessary. However, this is a challenging task, and there is a lack of methods addressing this gap. The GQM + Strategies ® approach effectively links goals and strategies on all levels of an organization by means of goal-oriented measurement. The approach is based on rationales for deciding about options when operationalizing goals and for evaluating the success of strategies with respect to goals. Keywords Need for Business AlignmentAlong with the growth in society's dependence on software and other forms of information technology (IT), the size and complexity of software systems have also grown. This has only magni-fied the cost, schedule, and quality concerns that have always plagued software development efforts. For decades, software engineering researchers and practitioners have attempted to control and reduce the costs of building software, to produce working software within shorter periods of time, and to increase the quality of the software produced. While great strides have been made in all three areas, the growth of software, along all dimensions (size, complexity, pervasiveness, criticality, etc.), has outpaced our ability to control all the factors related to its development.What has become clear, however, is that the issues related to software cost, schedule, and quality are inextricably linked with larger issues facing the businesses that develop the software. Such businesses come in a variety of flavors. Some are in the business of selling the software they develop to customers, either as custom-built software on contract or shrink-wrapped applications for some segment of the population. Others are in the business of selling some product or service, of which software is a significant component. Still others may only develop software to support their internal IT infrastructure, and do not sell software-related products. Some software organizations are not in business at all, but are non-profit organizations, government entities, or educational institutions. While all these organizational configurations provide quite different challenges to their development projects, the key here is that all software is developed within a larger business context, encompassing larger business goals, strategies, and measures of success.While all businesses employ various strategies to achieve their objectives, these objectives are not always stated explicitly or clearly enough to allow one to check whether or not ...

Abstract-Cost estimation is a vital task in most important software project decisions such as resource allocation and bidding. Analogy-based cost estimation is particularly transparent, as it relies on historical information from similar past projects, whereby similarities are determined by comparing the projects' key attributes and features. However, one crucial aspect of the analogy-based method is not yet fully accounted for: the different impact or weighting of a project's various features. Current approaches either try to find the dominant features or require experts to weight the features. Neither of these yields optimal estimation performance. Therefore, we propose to allocate separate weights to each project feature and to find the optimal weights by extensive search. We test this approach on several real-world data sets and measure the improvements with commonly used quality metrics. We find that this method 1) increases estimation accuracy and reliability, 2) reduces the model's volatility and, thus, is likely to increase its acceptance in practice, and 3) indicates upper limits for analogy-based estimation quality as measured by standard metrics.

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AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image-, speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state-of-the-art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.