human-computer interaction

“Failures” to be celebrated: an analysis of major pivots of software startups

In the context of software startups, project failure is embraced actively and considered crucial to obtain validated learning that can lead to pivots. A pivot is the strategic change of a business concept, product or the different elements of a business model. A better understanding is needed on different types of pivots and different factors that lead to failures and trigger pivots, for software entrepreneurial teams to make better decisions under chaotic and unpredictable environment. Due to the nascent nature of the topic, the existing research and knowledge on the pivots of software startups are very limited. In this study, we aimed at identifying the major types of pivots that software startups make during their startup processes, and highlighting the factors that fail software projects and trigger pivots. To achieve this, we conducted a case survey study based on the secondary data of the major pivots happened in 49 software startups. 10 pivot types and 14 triggering factors were identified. The findings show that customer need pivot is the most common among all pivot types. Together with customer segment pivot, they are common market related pivots. The major product related pivots are zoom-in and technology pivots. Several new pivot types were identified, including market zoom-in, complete and side project pivots. Our study also demonstrates that negative customer reaction and flawed business model are the most common factors that trigger pivots in software startups. Our study extends the research knowledge on software startup pivot types and pivot triggering factors. Meanwhile it provides practical knowledge to software startups, which they can utilize to guide their effective decisions on pivoting.

Software Development in Startup Companies: The Greenfield Startup Model

Software startups are newly created companies with no operating history and oriented towards producing cutting-edge products. However, despite the increasing importance of startups in the economy, few scientific studies attempt to address software engineering issues, especially for early-stage startups. If anything, startups need engineering practices of the same level or better than those of larger companies, as their time and resources are more scarce, and one failed project can put them out of business. In this study we aim to improve understanding of the software development strategies employed by startups. We performed this state-of-practice investigation using a grounded theory approach. We packaged the results in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible. This strategy allows startups to verify product and market fit, and to adjust the product trajectory according to early collected user feedback. The need to shorten time-to-market, by speeding up the development through low-precision engineering activities, is counterbalanced by the need to restructure the product before targeting further growth. The resulting implications of the GSM outline challenges and gaps, pointing out opportunities for future research to develop and validate engineering practices in the startup context.

How Do Software Startups Pivot? Empirical Results from a Multiple Case Study

In order to handle intense time pressure and survive in dynamic market, software startups have to make crucial decisions constantly on whether to change directions or stay on chosen courses, or in the terms of Lean Startup, to pivot or to persevere. The existing research and knowledge on software startup pivots are very limited. In this study, we focused on understanding the pivoting processes of software startups, and identified the triggering factors and pivot types. To achieve this, we employed a multiple case study approach, and analyzed the data obtained from four software startups. The initial findings show that different software startups make different types of pivots related to business and technology during their product development life cycle. The pivots are triggered by various factors including negative customer feedback.

Product-Focused Software Process Improvement

Exploring the outsourcing relationship in software startups: A multiple case study

Abstract -- Software startups are becoming increasingly popular in software industry as well as other sectors of economy. Startups that lack necessary competences often seek for external resources from outsourcing partners. Little is known how this outsourcing relationship works and whether it makes sense to outsource the technical competence to an external party. This is among the first investigations on the outsourcing relationships in software startups. By conducting exploratory case studies at six startups, we found a mixed experience with outsourcing. The experimental nature of an early product development makes outsourcing a feasible option, although startups often suffer from its uncertainty and managing commitments from partners. Results further propose that early contract-based activities could be transformed into a long-term partnership by adopting a startup boundary spanner's role, establishing an inter-personal relationship and maintaining a mutual commitment.1

On the Unhappiness of Software Developers

The happy-productive worker thesis states that happy workers are more productive. Recent research in software engineering supports the thesis, and the ideal of flourishing happiness among software developers is often expressed among industry practitioners. However, the literature suggests that a cost-effective way to foster happiness and productivity among workers could be to limit unhappiness. Psychological disorders such as job burnout and anxiety could also be reduced by limiting the negative experiences of software developers. Simultaneously, a baseline assessment of (un)happiness and knowledge about how developers experience it are missing. In this paper, we broaden the understanding of unhappiness among software developers in terms of (1) the software developer population distribution of (un)happiness, and (2) the causes of unhappiness while developing software. We conducted a large-scale quantitative and qualitative survey, incorporating a psychometrically validated instrument for measuring (un)happiness, with 2 220 developers, yielding a rich and balanced sample of 1318 complete responses. Our results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains. We also identified 219 factors representing causes of unhappiness while developing software. Our results, which are available as open data, can act as guidelines for practitioners in management positions and developers in general for fostering happiness on the job. We suggest considering happiness in future studies of both human and technical aspects in software engineering.

Towards Understanding Startup Product Development as Effectual Entrepreneurial Behaviors

Software startups face with multiple technical and business challenges, which could make the startup journey longer, or even become a failure. Little is known about entrepreneurial decision making as a direct force to startup development outcome. In this study, we attempted to apply a behaviour theory of entrepreneurial firms to understand the root-cause of some software startup s challenges. Six common challenges related to prototyping and product development in twenty software startups were identified. We found the behaviour theory as a useful theoretical lens to explain the technical challenges. Software startups search for local optimal solutions, emphasise on short-run feedback rather than long-run strategies, which results in vague prototype planning, paradox of demonstration and evolving throw-away prototypes. The finding implies that effectual entrepreneurial processes might require a more suitable product development approach than the current state-of-practice.

Building an entrepreneurship data warehouse

The main principle of the Lean Startup movement is that static business planning should be replaced by a dynamic development, where products, services, business model elements, business objectives and activities are frequently changed based on constant customer feedback. Our ambition is to empirically measure if such changes of the business idea, the business model elements, the project management and close interaction with customers really increases the success rate of entrepreneurs, and in what way. Our first paper; “Does Lean Startup really work? Foundation for an empirical study” presented the first attempt to model the relations we want to measure. This paper will focus on how to build and set up a test harness (from now on called the Entrepreneurship Platform or EP) to gather empirical data from Companies and how to store these data together with demographical and financial data from the PROFF-portal in the Entrepreneurial Data Warehouse (from now called the EDW). We will end the paper by discussing the potential methodological problems with our method, before we document a test run of our set-up to verify that we are actually able to populate the Data Warehouse with time series data.

Consequences of Unhappiness while Developing Software

The growing literature on affect among software developers mostly reports on the linkage between happiness, software quality, and developer productivity. Understanding the positive side of happiness – positive emotions and moods – is an attractive and important endeavor. Scholars in industrial and organizational psychology have suggested that also studying the negative side – unhappiness – could lead to cost-effective ways of enhancing working conditions, job performance, and to limiting the occurrence of psychological disorders. Our comprehension of the consequences of (un)happiness among developers is still too shallow, and is mainly expressed in terms of development productivity and software quality. In this paper, we attempt to uncover the experienced consequences of unhappiness among software developers. Using qualitative data analysis of the responses given by 181 questionnaire participants, we identified 49 consequences of unhappiness while doing software development. We found detrimental consequences on developers' mental well-being, the software development process, and the produced artifacts. Our classification scheme, available as open data, will spawn new happiness research opportunities of cause-effect type, and it can act as a guideline for practitioners for identifying damaging effects of unhappiness and for fostering happiness on the job.

What happens when software developers are (un)happy

The growing literature on affect among software developers mostly reports on the linkage between happiness, software quality, and developer productivity. Understanding happiness and unhappiness in all its components -- positive and negative emotions and moods -- is an attractive and important endeavor. Scholars in industrial and organizational psychology have suggested that understanding happiness and unhappiness could lead to cost-effective ways of enhancing working conditions, job performance, and to limiting the occurrence of psychological disorders. Our comprehension of the consequences of (un)happiness among developers is still too shallow, being mainly expressed in terms of development productivity and software quality. In this paper, we study what happens when developers are happy and unhappy while developing software. Qualitative data analysis of responses given by 317 questionnaire participants identified 42 consequences of unhappiness and 32 of happiness. We found consequences of happiness and unhappiness that are beneficial and detrimental for developers' mental well-being, the software development process, and the produced artifacts. Our classification scheme, available as open data enables new happiness research opportunities of cause-effect type, and it can act as a guideline for practitioners for identifying damaging effects of unhappiness and for fostering happiness on the job.

Unhappy Developers: Bad for Themselves, Bad for Process, and Bad for Software Product

Recent research in software engineering supports the "happy-productive" thesis, and the desire of flourishing happiness among programmers is often expressed by industry practitioners. Recent literature has suggested that a cost-effective way to foster happiness and productivity among workers could be to limit unhappiness of developers due to its negative impact. However, possible negative effects of unhappiness are still largely unknown in the software development context. In this paper, we present the first results from a study exploring the consequences of the unhappy developers. Using qualitative data analysis of the survey responses given by 181 participants, we identified 49 potential consequences of unhappiness while developing software. These results have several implications. While raising the awareness of the role of moods, emotions and feelings in software development, we foresee that our classification scheme will spawn new happiness studies linking causes and effects, and it can act as a guideline for developers and managers to foster happiness at work.

Achieving CMMI Level 2 with Enhanced Extreme Programming Approach

The relationship between agile methods and Software Engineering Institute's CMM approach is often debated. Some authors argue that the approaches are compatible, while others have criticized the application of agile methods from the CMM perspective. Only few CMM based assessments have been performed on projects using agile approaches. This paper explores an empirical case where a project using Extreme Programming (XP) based approach was assessed using the CMMI framework. The results provide empirical evidence pointing out that it is possible to achieve maturity level 2 with approach based on XP. Yet, the results confirm that XP, as it is defined, is not sufficient. This study demonstrates that it is possible to use the CMMI for assessing and improving agile processes. However, the analysis reveals that assessing an agile organization requires more interpretations than normally would be the case. It is further concluded that the CMMI model does not always support interpretations in an agile context.