Together with many success stories, promises such as the increase in production speed and the improvement in stakeholders’ collaboration have contributed to making agile a transformation in the software industry in which many companies want to take part. However, driven either by a natural and expected evolution or by contextual factors that challenge the adoption of agile methods as prescribed by their creator(s), software processes in practice mutate into hybrids over time. Are these still agile? In this article, we investigate the question: what makes a software development method agile? We present an empirical study grounded in a large-scale international survey that aims to identify software development methods and practices that improve or tame agility. Based on 556 data points, we analyze the perceived degree of agility in the implementation of standard project disciplines and its relation to used development methods and practices. Our findings suggest that only a small number of participants operate their projects in a purely traditional or agile manner (under 15 percent). That said, most project disciplines and most practices show a clear trend towards increasing degrees of agility. Compared to the methods used to develop software, the selection of practices has a stronger effect on the degree of agility of a given discipline. Finally, there are no methods or practices that explicitly guarantee or prevent agility. We conclude that agility cannot be defined solely at the process level. Additional factors need to be taken into account when trying to implement or improve agility in a software company. Finally, we discuss the field of software process-related research in the light of our findings and present a roadmap for future research.

Abstract Hardly any software development process is used as prescribed by authors or standards. Regardless of company size or industry sector, a majority of project teams and companies use hybrid development methods (short: hybrid methods) that combine different development methods and practices. Even though such hybrid methods are highly individualized, a common understanding of how to systematically construct synergetic practices is missing. In this article, we make a first step towards a statistical construction procedure for hybrid methods. Grounded in 1467 data points from a large‐scale practitioner survey, we study the question: What are hybrid methods made of and how can they be systematically constructed? Our findings show that only eight methods and few practices build the core of modern software development. Using an 85% agreement level in the participants' selections, we provide examples illustrating how hybrid methods can be characterized by the practices they are made of. Furthermore, using this characterization, we develop an initial construction procedure, which allows for defining a method frame and enriching it incrementally to devise a hybrid method using ranked sets of practice.

Among the multitude of software development processes available, hardly any is used by the book. Regardless of company size or industry sector, a majority of project teams and companies use customized processes that combine different development methods--so-called hybrid development methods. Even though such hybrid development methods are highly individualized, a common understanding of how to systematically construct synergetic practices is missing. In this paper, we make a first step towards devising such guidelines. Grounded in 1,467 data points from a large-scale online survey among practitioners, we study the current state of practice in process use to answer the question: What are hybrid development methods made of? Our findings reveal that only eight methods and few practices build the core of modern software development. This small set allows for statistically constructing hybrid development methods. Using an 85% agreement level in the participants' selections, we provide two examples illustrating how hybrid development methods are characterized by the practices they are made of. Our evidence-based analysis approach lays the foundation for devising hybrid development methods.

The digital transformation has brought us to a new horizon where devices, products and services are connected, interact with each other and generate massive amounts of data.What are the products and services to be offered in this environment?How will they be developed, delivered, maintained and evolved?Whatever the field, software, military, health care, or business, processes are everywhere.Not only are processes used to design, produce, and deliver enterprise products and services but also they are also used to instantiate enterprise/government practices, policies, and regulations.Fundamentally, processes are the means by which an enterprise satisfies customers and creates value, and they provide the building blocks of all information systems.Given their importance to the success and profitability of companies, it is more important than ever to seriously consider the design and improvement of these processes and to make sure that they are free of flaws and inconsistencies.At the same time, recent advances in the hardware domain have paved the way for more complex and critical systems.The Internet of things (IoT) applications and cloud computing infrastructures have introduced an increasing need for distributed system applications and more intelligent system segmentation.Big data solutions offer the potential to learn from vast amounts of information generated by these systems and processes (when collected).This information can be used to satisfy business needs or simply to learn and to improve systems and processes.The ICSSP 2017 conference aimed to explore how these new trends impact and constrain the way processes should be designed whatever the type of process and whatever the application domain.The conference also aimed to inquire how new visions and tools that incorporate cloud computing, big data, IoT, and DevOps could be incorporated into processes used to design next generation systems.To this end, ICSSP 2017 sought to address the following questions:1. What will the next generation of process paradigms look like? 2. How will the needs of all business and system stakeholders that participate in the development and evolution of systems and processes be addressed?and provided a venue for specific areas of interest including • Big data driven process modeling, assessment, and improvement • Mining software/business process repositories (including code, bug trackers, and etc) to improve processes • Dynamic process evolution, variability, and adaptability for next generation of process paradigms • Predictive monitoring and process deviance mining • Empirical evidence of the effectiveness of Agile/Lean practices and approaches in software, hardware, and hybrid systems development and evolution • Process issues in developing adaptive and evolving software systems Furthermore, we continued to explore topics that are core to the area of software and systems process including • Continuous process improvement in diverse areas and context

Non peer reviewed

Journal of Software: Evolution and ProcessVolume 29, Issue 11 e1869 SPECIAL ISSUE PAPER ICSSP 2016 – Special Issue Introduction Marco Kuhrmann, Corresponding Author Marco Kuhrmann [email protected] orcid.org/0000-0001-6101-8931 Clausthal University of Technology, Goslar, Germany Correspondence Clausthal University of Technology, Institute for Applied Software Systems Engineering, Wallstraße 6, 38640 Goslar, Germany. Email: [email protected] for more papers by this authorRory V. O'Connor, Rory V. O'Connor orcid.org/0000-0001-9253-0313 Dublin City University, Dublin, IrelandSearch for more papers by this authorDewayne E. Perry, Dewayne E. Perry University of Texas, Austin, TX, U.S.A.Search for more papers by this authorDavid Raffo, David Raffo Portland State University, Portland, OR, U.S.A.Search for more papers by this author Marco Kuhrmann, Corresponding Author Marco Kuhrmann [email protected] orcid.org/0000-0001-6101-8931 Clausthal University of Technology, Goslar, Germany Correspondence Clausthal University of Technology, Institute for Applied Software Systems Engineering, Wallstraße 6, 38640 Goslar, Germany. Email: [email protected] for more papers by this authorRory V. O'Connor, Rory V. O'Connor orcid.org/0000-0001-9253-0313 Dublin City University, Dublin, IrelandSearch for more papers by this authorDewayne E. Perry, Dewayne E. Perry University of Texas, Austin, TX, U.S.A.Search for more papers by this authorDavid Raffo, David Raffo Portland State University, Portland, OR, U.S.A.Search for more papers by this author First published: 08 November 2017 https://doi.org/10.1002/smr.1869Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume29, Issue11Special Issue: Process in Action: The Best Papers from The International Conference on Software and Systems Process 2016November 2017e1869 RelatedInformation

ABSTRACT Software process simulation (SPS) has been evolving over the past two decades after being introduced to the software engineering community in the 1980s. At that time the SPS technology attracted a great deal of interest from both academics and practitioners in the software process community—even to the extent of being one of the recommended techniques for achieving multiple Key Process Areas of Level 4 of the Capability Maturity Model Integration. However, in recent years, the growth of SPS seems to have slowed along with the number of reported applications in industry. This article summarizes the special panel that was held during ICSSP 2012 whose goals were to assess whether this technology remains applicable to today's software engineering projects and challenges and to point out the most beneficial opportunities for future research and industry application. Copyright © 2014 John Wiley & Sons, Ltd.

Summary form only given. Joint work with Maria Mateescu. Propagation models provide a framework for describing algorithms for the transient analysis of stochastic state transition systems, such as computing event probabilities, expectancies, and variances on biochemical reaction networks. We discuss the syntax, semantics, and pragmatics of propagation models. We give three use cases for propagation models: the chemical master equation, the reaction rate equation, and a hybrid method that combines these two equations. We present a propagation abstract data type (ADT) for implementing uniformization and integration algorithms on propagation models. The propagation ADT is based on an update operator, which propagates continuous mass values through a discrete state space. The update operator can be implemented using a threshold abstraction, which propagates only “significant” mass values and thus achieves a controllable compromise between efficiency and accuracy.

After more than 2 years of transitioning, our new periodical, the “Journal of Software: Evolution and Process” is born. The journal continues and enriches the tradition of the “Journal of Software Maintenance and Evolution: Research and Practice” and “Software Process: Improvement and Practice” that have successfully served their respective communities for many years. We will work to ensure that all the positive aspects of the parent journals remain in place, and to make changes that we believe will help the new journal to gain and sustain a leadership position. Nowadays, the way in which software systems are conceived, built, and managed is changing profoundly; open-source and commercial-off-the-shelf products, service-oriented architectures and cloud computing, mobile and pervasive applications, agile methods and lean organizations, DevOps, outsourcing and global development, software as service, value-based software engineering, benefit-driven development and usability engineering, and user enhanceable systems are a few examples of areas that have had an impact on software development, management, and evolution methods and processes. In this rapidly evolving scenario, we have the ambition to offer a new peer-reviewed archival journal that addresses the issues of software conception, development, management, evolution and improvement with a multidisciplinary view that encompasses technologies, processes, services, people, and organizations. The success of this enterprise depends primarily on the quality of papers we will attract: we welcome high-quality papers reporting theoretical findings, empirical investigations, practice reports and state-of-the-art reviews on all aspects of planning, designing, building and evolving large, complex software systems and improving the processes, capabilities and practices required to develop, manage and govern such systems. We also continue to welcome papers from SMEs about the implications of operating and competing in modern contexts and environments and about the adaptations that need to be made to standards and approach to continue to deliver value and improve performance in increasingly demanding environments. We are indebted to members of the outstanding international Advisory Editorial Board for their support and commitment. With this issue, it is our great pleasure to introduce two new members of the Board: Elisabetta Di Nitto, from the Politecnico di Milano, Italy, and Patricia Lago, of VU University, Amsterdam. The new appointments mean that our Board now consists of 54 international scholars from 16 countries. We would also like to take this opportunity to thank all the anonymous external reviewers who, together with the members of the Advisory Editorial Board, have reviewed the 129 manuscripts the journal received in 2011: the reputation of a journal greatly depends on the quality and rigor of its reviewing process, and we are grateful to our reviewers for continuing to provide high quality, constructive, and developmental review reports. We are confident that the “Journal of Software: Evolution and Process” will be able to continue the parent journals' long tradition of publishing high-quality and influential papers on theory and practice of continuous product, process, and service conception, development, management, evolution and improvement, while opening the door to new perspectives and insights.

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There is a critical need for cost effective independent verification and validation (IV & V). The goal of this research is to create a flexible tool that NASA IV & V can use to quantitatively assess the economic benefit of performing IV & V on NASA software development projects and to optimize that benefit across alternative IV & V plans. The tool is based on extensive research into software process simulation models (SPSMs) conducted at the Software Engineering Institute (SEI) by Watts Humphrey and Marc Kellner (1989), and Bill Curtis and others (1992). SPSMs can be used to quantify the costs and benefits associated with NASA IV & V practices enabling management to effectively allocate scarce resources for IV & V activities. In addition, SPSMs facilitate the IV & V of NASA software development processes by enabling checks and performance assessments.

In this paper, we present a "forward-looking" decision support framework that integrates timely metrics data with a simulation based defect model of the software development process in order to support software project management decisions regarding product quality. These predictions are evaluated using Outcome Based Control Limits (OBCLs) and Bi-Directional (both forward and reverse) simulation models of the software development process. The Bi-Directional models provide useful guidance to the program manager when evaluating possible corrective actions on a project that has gone outside the OBCLs. The forward model predicts the potential performance impacts of the proposed corrective actions. The reverse simulation models are new and take the desired system outcome as the starting point and represent the system as it evolves backward in time to identify the necessary starting point to achieve the desired outcome. The approach is presented using an illustrative example.

Software engineering (SE) truisms capture broadly-applicable principles of software construction. The trouble with truisms is that such general principles may not apply in specific cases. This paper tests the specificity of two SE truisms: (a) increasing software process level is a desirable goal; and (b) it is best to remove errors during the early parts of a software lifecycle. Our tests are based on two well-established SE models: (1) Boehm et.al.'s COCOMO II cost estimation model; and (2) Raffo's discrete event software process model of a software project life cycle. After extensive simulations of these models, the TAR2 treatment learner was applied to find the model parameters that most improved the potential performance of the real-world systems being modelled. The case studies presented here showed that these truisms are clearly sub-optimal for certain projects since other factors proved to be far more critical. Hence, we advise against truism-based process improvement. This paper offers a general alternative framework for model-based assessment of methods to improve software quality: modelling + validation + simulation + sensitivity. That is, after recording what is known in a model, that model should be validated, explored using simulations, then summarized to find the key factors that most improve model behavior.

In this paper, we present a decision support framework that integrates timely metrics data with simulation models of the software development process in order to support the software project management control function. This forward-looking approach provides predictions of project performance and the impact of various management decisions. Tradeoffs among performance measures are accomplished using Outcome Based Control Limits (OBCLs) and are augmented using multi-criteria utility functions and financial measures of performance to evaluate various process alternatives. The decision support framework enables the program manager to take corrective action as necessary on a project with the simulation model providing insight on potential performance impacts of the proposed corrective actions. A real world example is presented.