Towards a Software Tool Portal to Support Startup Process

Edison, Henry; Khanna, Dron; Bajwa, Sohaib Shahid; Brancaleoni, Valery; Bellettati, Luca

doi:10.1007/978-3-319-26844-6_43

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…There exists a wide range of various software tools to speed up the development processes in software startups. However, as Edison et al [59] suggest, there does not exist a clear understanding of how entrepreneurs can use the di↵erent tools e ciently to meet their specific needs. As to this, the paper describes the outline of a system that provides a software tool portal that supports and recommends which tools to use in the construction of software products and services.…”

Section: Discussionmentioning

confidence: 99%

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Software startup engineering: A systematic mapping study

Berg

Birkeland

Nguyen-Duc

et al. 2018

Journal of Systems and Software

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Context] Software startups have long been a significant driver in economic growth and innovation. The on-going failure of the major number of startups calls for a better understanding of state-of-the-practice of startup activities.[Objective] With a focus on engineering perspective, this study aims at identifying the change in focus of research area and thematic concepts operating startup research. [Method] A systematic mapping study on 74 primary papers (in which 27 papers are newly selected) from 1994 to 2017 was conducted with a comparison with findings from previous mapping studies. A classification schema was developed, and the primary studies were ranked according to their rigour. [Results] We discovered that most research has been conducted within the SWEBOK knowledge areas software engineering process, management, construction, design, and requirements, with the shift of focus towards process and management areas. We also provide an alternative classification for future startup research. We find that the rigour of the primary papers was assessed to be higher between 2013-2017 than that of 1994-2013. We also find an inconsistency of characterizing startups. [Conclusions] Future work can focus on certain research themes, such as startup evolution models and human aspects, and consolidate the thematic concepts describing software startups.

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Section: Discussionmentioning

confidence: 99%

“…Only one paper received low rigour score [59], as it didn't provide enough details about the data analysis and included no assessment of the validity of the results. However, as only the papers providing empirical evidence were assessed, it is possible that more papers would receive low rigour as well.…”

Section: Rigour Of Primary Studies 2013-2017mentioning

confidence: 99%

Software startup engineering: A systematic mapping study

Berg

Birkeland

Nguyen-Duc

et al. 2018

Journal of Systems and Software

View full text Add to dashboard Cite

show abstract

“…Startuppuccino started with the experience and observation of two team members who are also university teachers. The initial idea of the teachers was to recommend good software tools to initiate and support startups that miss key skills in their teams (e.g., design, web development) [26]. Commonly, early-stage startups lack resources and look for some startup tools in order to launch their idea and test the product solution fit.…”

Section: Casementioning

confidence: 99%

From MVPs to Pivots: A Hypothesis-Driven Journey of Two Software Startups

Khanna

Nguyen-Duc

2018

Lecture Notes in Business Information Processing

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Software startups have emerged as an interesting multiper-spective research area. Inspired by Lean Startup, a startup journey can be viewed as a series of experiments that validate a set of business hy-potheses an entrepreneurial team make explicitly or inexplicitly about their startup. It is little known about how startups evolve through busi-ness hypothesis testing. This study proposes a novel approach to look at the startup evolution as a Minimum Viable Product(MVP) creat-ing process. We identified relationships among business hypotheses and MVPs via ethnography and post-mortem analysis in two software star-tups. We observe that the relationship between hypotheses and MVPs is incomplete and non-linear in these two startups. We also find that entrepreneurs do learn from testing their hypotheses. However, there are hypotheses not tested by MVPs and vice versa, MVPs not related to any business hypothesis. The approach we proposed visualizes the flow of entrepreneurial knowledge across pivots via MVPs.

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“…Thus, the primary emphasis is on the use of development technologies, scalability, and thirdparty components [7,11]. While are few studies that describe software construction phenomena in detail in software startups [11], authors such as Edison et al [34] provide information on the use of tools during the construction phase. Furthermore, the authors in [1,11] found that some literature on software startups emphasizes the use of pair programming and software development standards during the later stages of the software construction phase as software project complexity rises.…”

mentioning

confidence: 99%