Abstract Agile software development methods have been increasingly adopted by many organizations at different organizational levels. Whether named agile adoption, agile transition, agile transformation, digital transformation or new ways of working, the success of embracing this change process mostly remains uncertain. This is primarily because there are many ways of evaluating success. Based on the existing agile assessment models, we developed a model of principles with associated practice clusters that serves as a core for a new agile assessment model that is capable of assessing agile organizations at different scale. Towards our ultimate goal to establish a lightweight, context-sensitive agile maturity model, we validated our initial findings in an expert interview study to identify improvement points, and ensure the at hand model’s applicability, coherence and relevance. The results of the interview study show that the structure as well as the content of our assessment model fits with the experts’ expectations and experience.

Agile software development is considered as a game changer by some [1]. There are others who approach the domain more skeptical [2]. Clearly, there is a gap in terms of how Agile is perceived. This gap could be reduced, if proper measures would have been timely employed. Agile maturity assessment models have been helpful in providing means and guidance for reducing this gap. Yet, a detailed look into existing models, comparative studies and literature reviews in the domain suggests that the agile maturity assessment models themselves are far from being mature. Further, the gap between what the proposed models offer and what industry really needs is frequently discussed by those comparative studies. In this study, we reflect on the existing agile maturity assessment models, compare them against a comprehensive set of criteria derived from the literature and from our background in assessing the capabilities of software development organizations. We conclude that none of the analyzed agile maturity assessment models are sound enough to be used in a practical context. Nevertheless, some of the models have interesting elements that can be reused for the development of a new agile assessment model.

Features in a software system usually must satisfy different quality expectations originating from the various stakeholders of a software system, ranging from direct users to the software manufacturer. As an example, the design quality of the source code will likely be more important if the associated feature is frequently used by customers or if it has strategic value for the software manufacturer. In order to effectively approach these qualitative subtleties, we need a means to specify non-functional requirements on the level of individual software features. Fine-grained specification of non-functional requirements on feature level respects their individual relevance for a feature and facilitates deriving suitable constraints for them. Particularly in DevOps-driven software projects with operational data the fulfillment of non-functional requirements for individual features can then continuously be evaluated. This guides software engineers in meeting these quality expectations on a much finer level than if treated uniformly across the software system. In this paper we present an extension of the QUAMOCO meta quality model for specifying and evaluating non-functional requirements on feature level. While the existing meta model focused on static quality measures, the extension of the meta model now also captures dynamic measures which accumulate during feature execution. An exploratory case study based on the results from an interview study shows the completeness of the approach for specifying and evaluating feature-dependent non-functional requirements.

Technical debt (TD) in a software system is a metaphor that tries to illustrate the remediation effort of the already introduced quality deficit and the impact thereof to the business value of the system. To address TD, various management activities are proposed, each addressing a particular purpose. Whereas the activities of debt identification and measurement are broadly considered in literature, the activities of debt prioritization and communication lack appropriate approaches with an economic perspective. This work proposes a TD prioritization approach. Therefore, it narrows down the focus of TD to design debt and relies on the quantification of design best practices. Further, the non-conformance of these practices is assessed by applying a benchmarking technique. As a result, the gained information is transferred into a portfolio-matrix to support the prioritization and communication of design remediation actions. The applicability and suitability of the approach are demonstrated by using the source code of the open source project GeoGebra.

The design debt metaphor tries to illustrate quality deficits in the design of a software and the impact thereof to the business value of the system. To pay off the debt, the literature offers various approaches for identifying and prioritizing these design flaws, but without proper support in aligning strategic improvement actions to the identified issues. This work addresses this challenge and examines the suitability of our proposed portfolio-based design assessment approach. Therefore, this investigation is conducted based on three case studies where the product source code was analyzed and assessed using our portfolio-based approach. As a result, the approach has proven to be able to recommend concrete and valuable design improvement actions that can be adapted to project constraints.

To measure object-oriented design quality, metric-based approaches have been established. These have then been enhanced by identifying design smells in code. While these approaches are useful for identifying hot spots that should be refactored, they are still too vague to sufficiently guide software developers to implement improvements. This is why our previous work focuses on measuring the compliance of source code with object-oriented design best practices. These design best practices were systematically derived from the literature and can be mapped to design principles, which can help reveal fundamental object-oriented design issues in a software product. Despite the successful applications of this approach in industrial and open source projects, there is little accepted knowledge about the importance of various design best practices. Consequently, this paper shows the result of an online survey aimed at identifying the importance of 49 design best practices on design quality. In total, 214 people participated in the survey, resulting in an average of 138 opinions for each practice. Based on these opinions, five very important, 21 important, 12 moderately important and 11 unimportant design best practices could be derived. This information about importance helps managing design improvements in a focused way.

Current software development trends have shortened release cycles and forced developers to implement short-term solutions that cannot cope with increasing product complexity. This phenomenon of introducing hasty design choices or applying bad design practices becomes something known as technical debt, in particular design debt. To pay off this debt, the literature offers approaches for identifying these design flaws; however, few methods for properly prioritizing investment efforts are available. In this paper, we propose an approach that supports the decision-making process regarding design improvements. It identifies violations of design best practices that are then arranged within a two-dimensional portfolio matrix. This matrix combines the importance of practices of design quality with actual achievement relative to a benchmark suite. To show the application of the approach in a quality-improvement process, we performed a feasibility study on three open-source projects and a benchmark suite containing 50 projects. This study clearly shows that the importance of the design best practices greatly impacts the improvement decisions and must be aligned with the strategic quality goals of the product.

IT-CISQ (Consortium for IT Software Quality) standard that was initiated and is driven by an OMG (Object Management Group) consortium claims to provide operational measures for a number of quality criteria of the ISO / IEC 25010 quality standard. In order to show the applicability of the IT-CISQ standard, we built a tool that provides automatic measures for the maintainability related measures of the IT-CISQ standard. We evaluated five Java open source projects with our IT-CISQ quality model and compared these results with the result of an expert-centered evaluation of the maintainability of these projects. The study shows that the IT-CISQ quality model for maintainability is well suited as a predictor for the quality criterion maintainability. Compared to our more comprehensive EMISQ (Expert Centered Method for Internal Software Quality) quality model (165 EMISQ measures vs. 18 IT-CISQ measures), the IT-CISQ quality model yields a similar order of projects. However, it is of course less suitable to give good guidance about improving aspects, as our more comprehensive EMISQ quality model includes considerably more maintainability related aspects of software. Furthermore, the tooling provided by us is innovative and could directly be used in practice by itself.

Algorithms for constructing hierarchical structures from user-generated metadata have caught the interest of the academic community in recent years. In social tagging systems, the output of these algorithms is usually referred to as folksonomies (from folk-generated taxonomies). Evaluation of folksonomies and folksonomy induction algorithms is a challenging issue complicated by the lack of golden standards, lack of comprehensive methods and tools as well as a lack of research and empirical/simulation studies applying these methods. In this article, we report results from a broad comparative study of state-of-the-art folksonomy induction algorithms that we have applied and evaluated in the context of five social tagging systems. In addition to adopting semantic evaluation techniques, we present and adopt a new technique that can be used to evaluate the usefulness of folksonomies for navigation . Our work sheds new light on the properties and characteristics of state-of-the-art folksonomy induction algorithms and introduces a new pragmatic approach to folksonomy evaluation, while at the same time identifying some important limitations and challenges of folksonomy evaluation. Our results show that folksonomy induction algorithms specifically developed to capture intuitions of social tagging systems outperform traditional hierarchical clustering techniques. To the best of our knowledge, this work represents the largest and most comprehensive evaluation study of state-of-the-art folksonomy induction algorithms to date.

While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question. (1) What distinctions of user motivations are identified by previous research, and in what ways are the motivations of users amenable to quantitative analysis? (2) To what extent does tagging motivation vary across different social tagging systems? (3) How does variability in user motivation influence resulting tags and folksonomies? In this paper, we present measures to detect whether a tagger is primarily motivated by categorizing or describing resources, and apply these measures to datasets from seven different tagging systems. Our results show that (a) users’ motivation for tagging varies not only across, but also within tagging systems, and that (b) tag agreement among users who are motivated by categorizing resources is significantly lower than among users who are motivated by describing resources. Our findings are relevant for (1) the development of tag-based user interfaces, (2) the analysis of tag semantics and (3) the design of search algorithms for social tagging systems.

Recent research provides evidence for the presence of emergent semantics in collaborative tagging systems. While several methods have been proposed, little is known about the factors that influence the evolution of semantic structures in these systems. A natural hypothesis is that the quality of the emergent semantics depends on the pragmatics of tagging: Users with certain usage patterns might contribute more to the resulting semantics than others. In this work, we propose several measures which enable a pragmatic differentiation of taggers by their degree of contribution to emerging semantic structures. We distinguish between categorizers, who typically use a small set of tags as a replacement for hierarchical classification schemes, and describers, who are annotating resources with a wealth of freely associated, descriptive keywords. To study our hypothesis, we apply semantic similarity measures to 64 different partitions of a real-world and large-scale folksonomy containing different ratios of categorizers and describers. Our results not only show that "verbose" taggers are most useful for the emergence of tag semantics, but also that a subset containing only 40% of the most 'verbose' taggers can produce results that match and even outperform the semantic precision obtained from the whole dataset. Moreover, the results suggest that there exists a causal link between the pragmatics of tagging and resulting emergent semantics. This work is relevant for designers and analysts of tagging systems interested (i) in fostering the semantic development of their platforms, (ii) in identifying users introducing "semantic noise", and (iii) in learning ontologies.

To increase area spectral efficiency, overlay use of spectrum already assigned to a primary system is considered in the context of cognitive radio research. Minimizing interference with the primary system is a key requirement and equivalent to the problem of transmission opportunity identification. Transmission opportunities are unused slots in the communication space which is spanned by time, frequency, and location. In this paper we describe a method to identify transmission opportunities by modeling the spatial power distribution with Gaussian processes. This approach to distributed sensing is verified with real-world measurements using the GSM downlink band as the primary system and contrasted with traditional wave propagation modeling approaches.

Zero-IF (direct conversion) receivers are very sensitive to non-linearities of analog hardware. A phase and/or amplitude imbalance between the inphase and quadrature signal branch results in a base band signal deterioration known as I/Q imbalance. Especially OFDM and the OFDM-based MC-CDMA modulation technique are affected by this error. This paper proposes a novel orthogonal quaternary MC-CDMA spreading code scheme that uses the inherent mathematical symmetry to mitigate I/Q imbalance without additional computational cost.