Background. Global Software Engineering (GSE) poses several challenges during the software design process. While the existing literature provides practices and guidelines to improve architecture knowledge management in GSE-based software organizations, the challenges related to organizing collaboration during the software design process still need attention. Aim. This study aims to identify the collaboration challenges faced by distributed teams during the software design process and provide recommendations. Method. We conducted semi-structured interviews with eight professionals working in different GSE-based organizations. Results. The study revealed three challenges that underscore the negative impact of the distributed setting on team collaboration during the software design process. Conclusion. We provide recommendations and propose future research direction to enhance team collaboration in the software design process for GSE-based organizations.

Agile software companies applying the DevOps approach require collaboration and information sharing between practitioners in various roles to produce value. Adopting new development practices affects how practitioners collaborate, requiring companies to form a closer connection between business strategy and software development. However, the types of information management, sales, and development needed to plan, evaluate features, and reconcile their expectations with each other need to be clarified. To support practitioners in collaborating and realizing changes to their practices, we investigated what information is needed and how it should be represented to support different stakeholders in their tasks. Compared to earlier research, we adopted a holistic approach—by including practitioners throughout the development process—to better understand the information needs from a broader viewpoint. We conducted six workshops and 12 semi-structured interviews at three Finnish small and medium-sized enterprises from different software domains. Thematic analysis was used to identify information-related issues and information and visualization needs for daily tasks. Three themes were constructed as the result of our analysis. Visual information representation catalyzes stakeholder discussion, and supporting information exchange between stakeholder groups is vital for efficient collaboration in software product development. Additionally, user-centric data collection practices are needed to understand how software products are used and to support practitioners’ daily information needs. We also found that a passive way of representing information, such as a dashboard that would disturb practitioners only when attention is needed, was preferred for daily information needs. The software engineering community should consider reviewing the information needs of practitioners from a more holistic view to better understand how tooling support can benefit information exchange between stakeholder groups when making product development decisions and how those tools should be built to accommodate different stakeholder views.

Data science projects have become commonplace over the last decade. During this time, the practices of running such projects, together with the tools used to run them, have evolved considerably. Furthermore, there are various studies on data science workflows and data science project teams. However, studies looking into both workflows and teams are still scarce and comprehensive works to build a holistic view do not exist. This study bases on a prior case study on roles and processes in data science. The goal here is to create a deeper understanding of data science projects and development processes. We conducted a survey targeted at experts working in the field of data science (n=50) to understand data science projects’ team structure, roles in the teams, utilized project management practices and the challenges in data science work. Results show little difference between big data projects and other data science. The found differences, however, give pointers for future research on how agile data science projects are, and how important is the role of supporting project management personnel. The current study is work in progress and attempts to spark discussion and new research directions.

Open-source projects commonly receive new feature requests from different types of users from layperson end users to developers, who actively contribute code to the project. However, the submission of new feature requests and the processes adopted for handling them is not always clear. In this work, we aim at investigating the availability of the contribution information, and in particular on the new feature requests, on 66 out of the 100 most starred GitHub projects. We examined the contribution guidelines and other documentation from those 66 projects. We particularly searched for whether the projects openly welcomed new contributions, such as feature requests. Our finding shows that even the most starred GitHub projects are often not reporting information on how to contribute and, in particular, how new feature requests are managed.

Understanding users, their requirements and usage patterns helps building better software. To continuously improve operations, user-interaction (U-I) data can provide developers with interesting new possibilities. This study aims at gaining an understanding of how software teams can start the use of U-I data and what challenges they face with it. In this paper, we describe three cases from different organizations. This includes explaining the activities, objectives, and challenges of each team in their efforts to begin using U-I data. We conducted the study with Action Design Research (ADR) method, resulting in findings from each case by intervening in the work of these teams. As a contribution, we designed a U-I data utilization method that summarizes teams' activities. Secondly, we refined and validated the categorizations of U-I data analysis and utilization objectives, and finally we categorized the challenges that the case teams faced. Together, these contributions lay out clear steps also for other software teams in starting the utilization of U-I data.

Modern software development is supported by a rich set of tools that accumulate data from the software process automatically. That data can be used for understanding and improving software processes without any manual data collection. In this paper we introduce an industrial case where data visualization of issue management system was used to investigate software projects. The results of the study show that visualization of issue management system data can really reveal deviations between planned process and executed process.

Global software engineering (GSE) has become common in the software industry. Distributed development work comes with many challenges, especially related to communication and coordination. Thus, it is essential to also teach and prepare the new population of software engineers to be aware of - and familiar with - these hurdles. We addressed this need by arranging a joint (agile) software project course between universities in Finland and Norway. We had three teams - one with students from both countries and two with students from the Norwegian university only. The students were given a teacher mentor, a handbook on agile practices and state-of-the-art tools for project management and software development. Apart from monitoring the teams' progress, we also collected data from emails, questionnaires, student reports and interviews during and after the project. The main lesson learned is that the global and local teams are mostly facing the same challenges - especially when it comes to team building, clear project roles, and communication and management issues. Ultimately, our results show that the challenges were harder to solve by the global team and that not solving challenges in a timely manner had more serious consequences.

Software process data is available in several tools such as version control systems, issue trackers and test and build systems to name a few. Using the data gathered in these software engineering tools would be ideal for collecting different kinds of software processes and product metrics as the data is already automatically gathered by the tools. However, the tools present and store the data in various formats. The data collection methods and interfaces also vary between the tools. This closes the software engineering data into silos and makes it hard to build reusable analysis and visualizations for the data. In this position paper we present a unified model for software engineering data and a framework for data collection, conversion and storing that utilizes our model. The aim of the model is to define a common format for software engineering data which is not dependent on specific software engineering tools or the software engineering process and thus can be used as a basis for building reusable visualization and analysis components. To demonstrate that we can build reusable visualization plugins on top of the framework, we created a timeline visualization plugin. The visualization plugin is used to visualize two data sets from industrial software projects that have different contexts and semantics.

Journal Article Techniques for Genetic Software Architecture Design Get access Outi Sievi-Korte, Outi Sievi-Korte * 1Department of Pervasive Computing, Tampere University of Technology, Korkeakoulunkatu 1, P.O. Box 553, 33101 Tampere, Finland *Corresponding author: [email protected] Search for other works by this author on: Oxford Academic Google Scholar Kai Koskimies, Kai Koskimies 1Department of Pervasive Computing, Tampere University of Technology, Korkeakoulunkatu 1, P.O. Box 553, 33101 Tampere, Finland Search for other works by this author on: Oxford Academic Google Scholar Erkki Mäkinen Erkki Mäkinen 2School of Information Sciences, University of Tampere, Kanslerinrinne 1, 33014 University of Tampere, Tampere, Finland Search for other works by this author on: Oxford Academic Google Scholar The Computer Journal, Volume 58, Issue 11, November 2015, Pages 3141–3170, https://doi.org/10.1093/comjnl/bxv049 Published: 14 July 2015 Article history Received: 01 December 2014 Revision received: 25 May 2015 Published: 14 July 2015

Genetic algorithms have become a popular heuristic technique to solve difficult search problems. However, in multi-objective problem solving, like software architecture generation, the basic variation mechanisms of genetic algorithms (mutation and crossover) tend to lead to mediocre solutions as the evolution favors balancing of several quality properties. In this paper, we explore the acceleration of genetic software architecture generation using a novel approach based on so-called quality farms, i.e., populations which favor a certain quality property. We hypothesize that by crossbreeding individuals from different quality farms it is possible to create beneficial variance that raises the fitness value to a significantly higher level. Experiments suggest that farm-based crossbreeding improves fitness value about 10%.

The automated synthesis of software architecture design and associated work allocation plan is considered, given the requirements of the system and a specification of the available, possibly distributed development teams. The technique applies genetic algorithms, with mutations introducing changes both in the architectural solutions and in the work allocation schemes. The technique is implemented and evaluated using a non-trivial example system, the control system of an electronic home. The results suggest that genetic algorithms are a viable approach to solve this kind of multi-targeted optimization problem, assuming that the architectural quality and work allocation fitness can be given appropriate metrics.

Automated support for software architecture design is discussed. The proposed approach is based on a tool applying genetic algorithms for producing potential architecture proposals. The tool requires a basic functional decomposition of the system and the specification of the quality requirements as input, relying on a repository of standard solutions like patterns and architectural styles. The underlying techniques and the design of the tool are discussed, and the usage of the tool is illustrated by an example.

Techniques exist to synthesize software architecture using genetic algorithms that employ transformations based on mutations and crossover. In this paper, we demonstrate that complementary crossover can significantly improve this technique. We study two versions of complementary crossover, one in which parents are selected so that they complement each other but the genes are inherited randomly from the parents, and another in which the genes are inherited in a more purposeful way. Empirical studies on two sample systems suggest that the complementary crossover outperforms the traditional crossover in genetic software architecture synthesis especially in the presence of mutations that provide delayed reward.

Software architecture design can be regarded as finding an optimal combination of known general solutions and architectural knowledge with respect to given requirements. Based on previous work on synthesizing software architecture using genetic algorithms, we propose a refined fitness function for assessing software architecture in genetic synthesis, taking into account the specific anticipated needs of the software system under design. Inspired by real life architecture evaluation methods, the refined fitness function employs scenarios, specific situations possibly occurring during the lifetime of the system and requiring certain modifiability properties of the system. Empirical studies based on two example systems suggest that using this kind of fitness function significantly improves the quality of the resulting architecture.