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Large critical systems, such as those created in the space domain, are usually developed by a large number of organizations and, furthermore, they have to comply with standards. Yet, the different stakeholders often do not have a common understanding of the needed quality of requirements specifications. Achieving such a common understanding is a laborious process that is currently not sufficiently supported. Moreover, such a common understanding must be aligned with the standards. In this paper, we present an approach that can be used to align the different stakeholder perceptions regarding the quality of requirements specifications. Existing quality models for requirements specifications are analyzed for equivalences, and transferred into a common representation, the so-called Aligned Quality Map (AQM). Furthermore, a process is defined that supports the alignment of different stakeholder perspectives with regard to the quality of requirements specifications using AQM, which is validated in a case study in the context of European space projects. AQM has been created and populated with an initial set of quality models. It is designed in such way that it can be extended to include further quality models. The case study has shown that an alignment of different stakeholder perspectives and the quality model of the European Cooperation for Space Standardization using AQM is feasible. The approach allows for aligning different stakeholder perspectives for a common understanding of the quality of requirements specifications in the context of standards. Furthermore, AQM supports the assessment of requirements specifications.
Software development teams have to face stress caused by deadlines, staff turnover, or individual differences in commitment, expertise, and time zones. While students are typically taught the theory of software project management, their exposure to such stress factors is usually limited. However, preparing students for the stress they will have to endure once they work in project teams is important for their own sake, as well as for the sake of team performance in the face of stress. Team performance has been linked to the diversity of software development teams, but little is known about how diversity influences the stress experienced in teams. In order to shed light on this aspect, we provided students with the opportunity to self-experience the basics of project management in self-organizing teams, and studied the impact of six diversity dimensions on team performance, coping with stressors, and positive perceived learning effects. Three controlled experiments at two universities with a total of 65 participants suggest that the social background impacts the perceived stressors the most, while age and work experience have the highest impact on perceived learnings. Most diversity dimensions have a medium correlation with the quality of work, yet no significant relation to the team performance. This lays the foundation to improve students’ training for software engineering teamwork based on their diversity-related needs and to create diversity-sensitive awareness among educators, employers and researchers.
For large-scale processes as implemented in organizations that develop software in regulated domains, comprehensive software process models are implemented, e.g., for compliance requirements. Creating and evolving such processes is demanding and requires software engineers having substantial modeling skills to create consistent and certifiable processes. While teaching process engineering to students, we observed issues in providing and explaining models. In this paper, we present an exploratory study in which we aim to shed light on the challenges students face when it comes to modeling. Our findings show that students are capable of doing basic modeling tasks, yet, fail in utilizing models correctly. We conclude that the required skills, notably abstraction and solution development, are underdeveloped due to missing practice and routine. Since modeling is key to many software engineering disciplines, we advocate for intensifying modeling activities in teaching.
Startups play a key role in software-based innovation. They make an important contribution to an economy’s ability to compete and innovate, and their importance will continue to grow due to increasing digitalization. However, the success of a startup depends primarily on market needs and the ability to develop a solution that is attractive enough for customers to choose. A sophisticated technical solution is usually not critical, especially in the early stages of a startup. It is not necessary to be an experienced software engineer to start a software startup. However, this can become problematic as the solution matures and software complexity increases. Based on a proposed solution for systematic software development for early-stage startups, in this paper, we present the key findings of a survey study to identify the methodological and technical priorities of software startups. Among other things, we found that requirements engineering and architecture pose challenges for startups. In addition, we found evidence that startups’ software development approaches do not tend to change over time. An early investment in a more scalable development approach could help avoid long-term software problems. To support such an investment, we propose an extended model for Entrepreneurial Software Engineering that provides a foundation for future research.
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%). 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.
Software is an integrated part of new features within the automotive sector, car manufacturers, the Hersteller Initiative Software (HIS) consortium defined metrics to determine software quality. Yet, problems with assigning metrics to quality attributes often occur in practice. The specified boundary values lead to discussions between contractors and clients as different standards and metric sets are used. This paper studies metrics used in the automotive sector and the quality attributes they address. The HIS, ISO/IEC 25010:2011, and ISO/IEC 26262:2018 are utilized to draw a big picture illustrating (i) which metrics and boundary values are reported in literature, (ii) how the metrics match the standards, (iii) which quality attributes are addressed, and (iv) how the metrics are supported by tools. Our findings from analyzing 38 papers include a catalog of 112 metrics of which 17 define boundary values and 48 are supported by tools. Most of the metrics are concerned with source code, are generic, and not specifically designed for automotive software development. We conclude that many metrics exist, but a clear definition of the metrics' context, notably regarding the construction of flexible and efficient measurement suites, is missing.
Context:
Test-driven development (TDD) is an agile software development approach that has been widely claimed to improve software quality. However, the extent to which TDD improves quality appears to be largely dependent upon the characteristics of the study in which it is evaluated (e.g., the research method, participant type, programming environment, etc.). The particularities of each study make the aggregation of results untenable.
Objectives:
The goal of this paper is to: increase the accuracy and generalizability of the results achieved in isolated experiments on TDD, provide joint conclusions on the performance of TDD across different industrial and academic settings, and assess the extent to which the characteristics of the experiments affect the quality-related performance of TDD.
Method:
We conduct a family of 12 experiments on TDD in academia and industry. We aggregate their results by means of meta-analysis. We perform exploratory analyses to identify variables impacting the quality-related performance of TDD.
Results:
TDD novices achieve a slightly higher code quality with iterative test-last development (i.e., ITL, the reverse approach of TDD) than with TDD. The task being developed largely determines quality. The programming environment, the order in which TDD and ITL are applied, or the learning effects from one development approach to another do not appear to affect quality. The quality-related performance of professionals using TDD drops more than for students. We hypothesize that this may be due to their being more resistant to change and potentially less motivated than students.
Conclusion:
Previous studies seem to provide conflicting results on TDD performance (i.e., positive vs. negative, respectively). We hypothesize that these conflicting results may be due to different study durations, experiment participants being unfamiliar with the TDD process, or case studies comparing the performance achieved by TDD vs. the control approach (e.g., the waterfall model), each applied to develop a different system. Further experiments with TDD experts are needed to validate these hypotheses.
Entrepreneurial software engineering: towards a hybrid development method for early-stage startups
(2021)
A considerable share of innovative software-intensive products is developed by startups. However, product development in an early-stage startup is not a sequential process. A business idea is usually based on a number of assumptions. The riskiest assumptions need to be tested. Depending on the test results, a product strategy may change several times. This raises the question of how to create sufficiently stable software using engineering principles despite a dynamic product strategy that is subject to many uncertainties. Hybrid development methods that combine agile aspects with classical engineering methods seem to be a good choice in such a start-up context. This paper proposes a lightweight hybrid development method that provides early-stage startups with a framework to support the development of single-feature minimum viable products. The method was derived from a start-up company's founding case and evaluated in expert interviews. The proposed method is intended to provide a basis for discussion between practitioners and scientists with the aim of better understanding the application of software engineering principles in software start-ups.
The emergence of agile methods and practices has not only changed the development processes but might also have affected how companies conduct software process improvement (SPI). Through a set of complementary studies, we aim to understand how SPI has changed in times of agile software development. Specifically, we aim (1) to identify and characterize the set of publications that connect elements of agility to SPI, (2) to explore to which extent agile methods/practices have been used in the context of SPI, and (3) to understand whether the topics addressed in the literature are relevant and useful for industry professionals. To study these questions, we conducted an in-depth analysis of the literature identified in a previous mapping study, an interview study, and an analysis of the responses given by industry professionals to SPI-related questions stemming from an independently conducted survey study.
Selecting a suitable development method for a specific project context is one of the most challenging activities in process design. To extend the so far statistical construction of hybrid development methods, we analyze 829 data points to investigate which context factors influence the choice of methods or practices. Using exploratory factor analysis, we derive five base clusters consisting of up to 10 methods. Logistic regression analysis then reveals which context factors have an influence on the integration of methods from these clusters in the development process. Our results indicate that only a few context factors including project/product size and target application domain significantly influence the choice. This summary refers to the paper “Determining Context Factors for Hybrid Development Methods with Trained Models”. This paper was published in the proceedings of the International Conference on Software and System Process in 2020.