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The troubles began when Tom, the business analyst, asked the customer what he wants. The customer came up with good ideas for software features. Tom created a brilliant roadmap and defined the requirements for a new software product. Mary, the development team leader, was already eager to start developing and happy when she got the requirements. She and her team went ahead and created the software right away. Afterwards, Paul tested the software against the requirements. As soon as the software fulfilled the requirements, Linda, the product manager, deployed it to the customer. The customer did not like the software and ignored it. Ringo, the head of software development, was fired. How come? Nowadays, we have tremendous capabilities for creating nearly all kinds of software to fulfill the needs of customers. We can apply agile practices for reacting flexibly to changing requirements, we can use distributed development, open source, or other means for creating software at low cost, we can use cloud technologies for deploying software rapidly, and we can get enormous amounts of data showing us how customers actually use software products. However, the sad reality is that around 90% of products fail, and more than 60% of the features of a typical software product are rarely or never used. But there is a silver lining – an insight regarding successful features: Around 60% of the successes stem from a significant change of an initial idea. This gives us a hint on how to build the right software for users and customers.
Software Process Improvement (SPI) programs have been implemented, inter alia, to improve quality and speed of software development. SPI addresses many aspects ranging from individual developer skills to entire organizations. It comprises, for instance, the optimization of specific activities in the software lifecycle as well as the creation of organizational awareness and project culture. In the course of conducting a systematic mapping study on the state-of-the-art in SPI from a general perspective, we observed Software Quality Management (SQM) being of certain relevance in SPI programs. In this paper, we provide a detailed investigation of those papers from the overall systematic mapping study that were classified as addressing SPI in the context of SQM (including testing). From the main study’s result set, 92 papers were selected for an in-depth systematic review to study the contributions and to develop an initial picture of how these topics are addressed in SPI. Our findings show a fairly pragmatic contribution set in which different solutions are proposed, discussed, and evaluated. Among others, our findings indicate a certain reluctance towards standard quality or (test) maturity models and a strong focus on custom review, testing, and documentation techniques, whereas a set of five selected improvement measures is almost equally addressed.
Context: Companies need capabilities to evaluate the customer value of software intensive products and services. One way of systematically acquiring data on customer value is running continuous experiments as part of the overall development process. Objective: This paper investigates the first steps of transitioning towards continuous experimentation in a large company, including the challenges faced. Method: We conduct a single-case study using participant observation, interviews, and qualitative analysis of the collected data. Results: Results show that continuous experimentation was well received by the practitioners and practising experimentation helped them to enhance understanding of their product value and user needs. Although the complexities of a large multi-stakeholder business to-business (B2B) environment presented several challenges such as inaccessible users, it was possible to address impediments and integrate an experiment in an ongoing development project. Conclusion: Developing the capability for continuous experimentation in large organisations is a learning process which can be supported by a systematic introduction approach with the guidance of experts. We gained experience by introducing the approach on a small scale in a large organisation, and one of the major steps for future work is to understand how this can be scaled up to the whole development organisation.
Context: The current transformation of automotive development towards innovation, permanent learning and adapting to changes are directing various foci on the integration of agile methods. Although, there have been efforts to apply agile methods in the automotive domain for many years, a wide-spread adoption has not yet taken place.
Goal: This study aims to gain a better understanding of the forces that prevent the adoption of agile methods.
Method: Survey based on 16 semi-structured interviews from the automotive domain. The results are analyzed by means of thematic coding.
Results: Forces that prevent agile adoption are mainly of organizational, technical and social nature and address inertia, anxiety and context factors. Key challenges in agile adoption are related to transforming organizational structures and culture, achieving faster software release cycles without loss of quality, the importance of software reuse in combination with agile practices, appropriate quality assurance measures, and the collaboration with suppliers and other disciplines such as mechanics.
Conclusion: Significant challenges are imposed by specific characteristics of the automotive domain such as high quality requirements and many interfaces to surrounding rigid and inflexible processes. Several means are identified that promise to overcome these challenges.
Managing software process evolution : traditional, agile and beyond - how to handle process change
(2016)
This book focuses on the design, development, management, governance and application of evolving software processes that are aligned with changing business objectives, such as expansion to new domains or shifting to global production. In the context of an evolving business world, it examines the complete software process lifecycle, from the initial definition of a product to its systematic improvement. In doing so, it addresses difficult problems, such as how to implement processes in highly regulated domains or where to find a suitable notation system for documenting processes, and provides essential insights and tips to help readers manage process evolutions. And last but not least, it provides a wealth of examples and cases on how to deal with software evolution in practice.
Reflecting these topics, the book is divided into three parts. Part 1 focuses on software business transformation and addresses the questions of which process(es) to use and adapt, and how to organize process improvement programs. Subsequently, Part 2 mainly addresses process modeling. Lastly, Part 3 collects concrete approaches, experiences, and recommendations that can help to improve software processes, with a particular focus on specific lifecycle phases.
This book is aimed at anyone interested in understanding and optimizing software development tasks at their organization. While the experiences and ideas presented will be useful for both those readers who are unfamiliar with software process improvement and want to get an overview of the different aspects of the topic, and for those who are experts with many years of experience, it particularly targets the needs of researchers and Ph.D. students in the area of software and systems engineering or information systems who study advanced topics concerning the organization and management of (software development) projects and process improvements projects.
Information Systems in Distributed Environment (ISDE) is becoming a prominent standard in this globalization era due to advancement in information and communication technologies. The advent of the internet has supported Distributed Software Development (DSD) by introducing new concepts and opportunities, resulting in benefits such as scalability, flexibility, interdependence, reduced cost, resource pools, and usage tracking. The distributed development of information systems as well as their deployment and operation in distributed environments impose new challenges for software organizations and can lead to business advantages. In distributed environments, business units collaborate across time zones, organizational boundaries, work cultures and geographical distances, something that ultimately has led to an increasing diversification and growing complexity of cooperation among units. The real-world practice of developing, deployment and operation of information systems in globally distributed projects has been viewed from various perspectives, though technical and engineering in conjunction with managerial and organizational viewpoints have dominated the researcher’s attention so far. Successful participation in distributed environments, however, is ultimately a matter of the participants understanding and exploiting the particularities of their respective local contexts at specific points in time and exploring practical solutions through the local resources available.
This special issue of the Computer standards & interfaces journal therefore includes papers received from the public call for papers and extended and improved versions of those papers that were selected from the best of the International Workshop on Information Systems in Distributed Environment (ISDE 2014). It aims to serve as a forum to bring together academics, researchers, practitioners and students in the field of distributed information system, by presenting novel developments and lesson learned from real world cases, and to promote the exchange of ideas, discussion and advancement in these areas.
Die Kombination von Softwareproduktlinien und agiler Softwareentwicklung in der Automobilbranche ist vielversprechend. Das Ziel ist hierbei, sowohl die Vorteile agiler Methoden wie kurze Entwicklungszyklen als auch die Vorteile systematischer Wiederverwendung wie beispielsweise das effektive Management von Varianten zu erzielen. Allerdings ist die Kombination auch mit Herausforderungen verbunden und erfordert eine geeignete Einführungs- oder Transformationsstrategie. Basierend auf Erkenntnissen einer Interviewstudie und existierenden Produktlinienentwicklungen werden Herausforderungen und Lösungsideen aufgezeigt.
Analysis of multicellular patterns is required to understand tissue organizational processes. By using a multi-scale object oriented image processing method, the spatial information of cells can be extracted automatically. Instead of manual segmentation or indirect measurements, such as general distribution of contrast or flow, the orientation and distribution of individual cells is extracted for quantitative analysis. Relevant objects are identified by feature queries and no low-level knowledge of image processing is required.
Data collected from internet applications are mainly stored in the form of transactions. All transactions of one user form a sequence, which shows the user´s behaviour on the site. Nowadays, it is important to be able to classify the behaviour in real time for various reasons: e.g. to increase conversion rate of customers while they are in the store or to prevent fraudulent transactions before they are placed. However, this is difficult due to the complex structure of the data sequences (i.e. a mix of categorical and continuous data types, constant data updates) and the large amounts of data that are stored. Therefore, this thesis studies the classification of complex data sequences. It surveys the fields of time series analysis (temporal data mining), sequence data mining or standard classification algorithms. It turns out that these algorithms are either difficult to be applied on data sequences or do not deliver a classification: Time series need a predefined model and are not able to handle complex data types; sequence classification algorithms such as the apriori algorithm family are not able to utilize the time aspect of the data. The strengths and weaknesses of the candidate algorithms are identified and used to build a new approach to solve the problem of classification of complex data sequences. The problem is thereby solved by a two-step process. First, feature construction is used to create and discover suitable features in a training phase. Then, the blueprints of the discovered features are used in a formula during the classification phase to perform the real time classification. The features are constructed by combining and aggregating the original data over the span of the sequence including the elapsed time by using a calculated time axis. Additionally, a combination of features and feature selection are used to simplify complex data types. This allows catching behavioural patterns that occur in the course of time. This new proposed approach combines techniques from several research fields. Part of the algorithm originates from the field of feature construction and is used to reveal behaviour over time and express this behaviour in the form of features. A combination of the features is used to highlight relations between them. The blueprints of these features can then be used to achieve classification in real time on an incoming data stream. An automated framework is presented that allows the features to adapt iteratively to a change in underlying patterns in the data stream. This core feature of the presented work is achieved by separating the feature application step from the computational costly feature construction step and by iteratively restarting the feature construction step on the new incoming data. The algorithm and the corresponding models are described in detail as well as applied to three case studies (customer churn prediction, bot detection in computer games, credit card fraud detection). The case studies show that the proposed algorithm is able to find distinctive information in data sequences and use it effectively for classification tasks. The promising results indicate that the suggested approach can be applied to a wide range of other application areas that incorporate data sequences.
This book showcases new and innovative approaches to biometric data capture and analysis, focusing especially on those that are characterized by non-intrusiveness, reliable prediction algorithms, and high user acceptance. It comprises the peer-reviewed papers from the international workshop on the subject that was held in Ancona, Italy, in October 2014 and featured sessions on ICT for health care, biometric data in automotive and home applications, embedded systems for biometric data analysis, biometric data analysis: EMG and ECG, and ICT for gait analysis. The background to the book is the challenge posed by the prevention and treatment of common, widespread chronic diseases in modern, aging societies. Capture of biometric data is a cornerstone for any analysis and treatment strategy. The latest advances in sensor technology allow accurate data measurement in a non-intrusive way, and in many cases it is necessary to provide online monitoring and real-time data capturing to support a patient’s prevention plans or to allow medical professionals to access the patient’s current status. This book will be of value to all with an interest in this expanding field.