A Feature-Based Tool-Selection Classification for Agile Software Development

Abstract-With the advancement in technology, software development complexities are rising across the globe. This trend is forcing companies and organizations to adopt management methods and tools to accelerate time to market, more easily manage changing priorities, increase the customer satisfaction and reduce product expenses. Agile software development methods offer a solution to these issues, but problems remain over evaluation along with the offering of the correct agile software as well as a collection of agile tools. The purpose of this paper is to introduce best tools and features, criteria used for evaluating currently existing tools and propose a classification model to right agile tool selection. To prepare a list of the best tools and their features in the market, a practical research on existing tools and their features were performed. Finally, a classification model was prepared and the results show which tools best fit into different level of maturity in projects and companies

CoFeD: A visualisation framework for comparative quality evaluation

Evaluation for the purpose of selection can be a challenging task particularly when there is a plethora of choices available. Short-listing, comparisons and eventual choice(s) can be aided by visualisation techniques. In this paper we use Feature Analysis, Tabular and Tree Representations and Composite Features Diagrams (CFDs) for profiling user requirements and for top-down profiling and evaluation of items (methods, tools, techniques, processes and so on) under evaluation. The resulting framework CoFeD enables efficient visual comparison and initial short-listing. The second phase uses bottom-up quantitative evaluation which aids the elimination of the weakest items and hence the effective selection of the most appropriate item. The versatility of the framework is illustrated by a case study comparison and evaluation of two agile methodologies. The paper concludes with limitations and indications of further work

Built to Last or Built Too Fast? Evaluating Prediction Models for Build Times

Automated builds are integral to the Continuous Integration (CI) software development practice. In CI, developers are encouraged to integrate early and often. However, long build times can be an issue when integrations are frequent. This research focuses on finding a balance between integrating often and keeping developers productive. We propose and analyze models that can predict the build time of a job. Such models can help developers to better manage their time and tasks. Also, project managers can explore different factors to determine the best setup for a build job that will keep the build wait time to an acceptable level. Software organizations transitioning to CI practices can use the predictive models to anticipate build times before CI is implemented. The research community can modify our predictive models to further understand the factors and relationships affecting build times.Comment: 4 paged version published in the Proceedings of the IEEE/ACM 14th International Conference on Mining Software Repositories (MSR) Pages 487-490. MSR 201

What influences the speed of prototyping? An empirical investigation of twenty software startups

It is essential for startups to quickly experiment business ideas by building tangible prototypes and collecting user feedback on them. As prototyping is an inevitable part of learning for early stage software startups, how fast startups can learn depends on how fast they can prototype. Despite of the importance, there is a lack of research about prototyping in software startups. In this study, we aimed at understanding what are factors influencing different types of prototyping activities. We conducted a multiple case study on twenty European software startups. The results are two folds, firstly we propose a prototype-centric learning model in early stage software startups. Secondly, we identify factors occur as barriers but also facilitators for prototyping in early stage software startups. The factors are grouped into (1) artifacts, (2) team competence, (3) collaboration, (4) customer and (5) process dimensions. To speed up a startups progress at the early stage, it is important to incorporate the learning objective into a well-defined collaborative approach of prototypingComment: This is the author's version of the work. Copyright owner's version can be accessed at doi.org/10.1007/978-3-319-57633-6_2, XP2017, Cologne, German

Boundary Objects and their Use in Agile Systems Engineering

Agile methods are increasingly introduced in automotive companies in the attempt to become more efficient and flexible in the system development. The adoption of agile practices influences communication between stakeholders, but also makes companies rethink the management of artifacts and documentation like requirements, safety compliance documents, and architecture models. Practitioners aim to reduce irrelevant documentation, but face a lack of guidance to determine what artifacts are needed and how they should be managed. This paper presents artifacts, challenges, guidelines, and practices for the continuous management of systems engineering artifacts in automotive based on a theoretical and empirical understanding of the topic. In collaboration with 53 practitioners from six automotive companies, we conducted a design-science study involving interviews, a questionnaire, focus groups, and practical data analysis of a systems engineering tool. The guidelines suggest the distinction between artifacts that are shared among different actors in a company (boundary objects) and those that are used within a team (locally relevant artifacts). We propose an analysis approach to identify boundary objects and three practices to manage systems engineering artifacts in industry

Modeling of system knowledge for efficient agile manufacturing : tool evaluation, selection and implementation scenario in SMEs

In the manufacturing world, knowledge is fundamental in order to achieve effective and efficient real time decision making. In order to make manufacturing system knowledge available to the decision maker it has to be first captured and then modelled. Therefore tools that provide a suitable means for capturing and representation of manufacturing system knowledge are required in several types of industrial sectors and types of company’s (large, SME). A literature review about best practice for capturing requirements for simulation development and system knowledge modeling has been conducted. The aim of this study was to select the best tool for manufacturing system knowledge modelling in an open-source environment. In order to select this tool, different criteria were selected, based on which several tools were analyzed and rated. An exemplary use case was then developed using the selected tool, Systems Modeling Language (SysML). Therefore, the best practice has been studied, evaluated, selected and then applied to two industrial use cases by the use of a selected opens source tool.peer-reviewe

International conference on software engineering and knowledge engineering: Session chair

The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing. The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome