FLOSSmole

Howison, James; Conklin, Megan; Crowston, Kevin

doi:10.4018/jitwe.2006070102

Cited by 102 publications

(4 citation statements)

References 4 publications

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“…The second insight into constructing a BN capable of assisting in RE tasks is to take the benefits of automatic learning processes from metrics databases. Nonetheless, even though for design and implementation, there are many repositories (e.g., GitHub, Sourceforge) that can be mined, or metrics databases that can be accessed directly (e.g., [34]) at the requirements level, these kinds of resources are not easily available.…”

Section: Rq2mentioning

confidence: 98%

Bayesian networks for enhancement of requirements engineering: a literature review

Cano

2015

Requirements Eng

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Requirements analysis is the software engineering stage that is closest to the users' world. It also involves tasks that are knowledge intensive. Thus, the use of Bayesian networks (BNs) to model this knowledge would be a valuable aid. These probabilistic models could manage the imprecision and ambiguities usually present in requirements engineering (RE). In this work, we conduct a literature review focusing on where and how BNs are applied on subareas of RE in order to identify which gaps remain uncovered and which methods might engineers employ to incorporate this intelligent technique into their own requirements processes. The scarcity of identified studies (there are only 20) suggests that not all RE areas have been properly investigated in the literature. The evidence available for adopting BNs into RE is sufficiently mature yet the methods applied are not easily translatable to other topics. Nonetheless, there are enough studies supporting the applicability of synergistic cooperation between RE and BNs. This work provides a background for understanding the current state of research encompassing RE and BNs. Functional, non-functional and -ilities requirements artifacts are enhanced by the use of BNs. These models were obtained by interacting with experts or by learning from databases. The most common criticism from the point of view of BN experts is that the models lack validation, whereas requirements engineers point to the lack of a clear application method for BNs and the lack of tools for incorporating them as built-in help functions.

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Section: Rq2mentioning

confidence: 98%

Bayesian networks for enhancement of requirements engineering: a literature review

Cano

2015

Requirements Eng

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“…Second, the results of this email artifact survey can guide the leaders of FLOSS data repositories. Three such repositories are FLOSSmole (Howison et al, 2006), SRDA (Van Antwerp & Madey, 2008), and FLOSSMetrics (Herraiz et al, 2009). Results here can indicate which artifacts are being used as data in the field, as reflected in its literature, so that these repositories are collecting and storing the emails in a way that is most helpful to the research community.…”

Section: Introductionmentioning

confidence: 93%

“…A final option that has been less viable in the past but is growing in importance more recently is the use of a centralized FLOSS data repository to collect the emails on behalf of the researcher, and store the results for others to use. Examples of this type of open data collection and sharing infrastructure in the FLOSS community include FLOSSmole (Howison et al, 2006) and SRDA (Van Antwerp & Madey, 2008) both of which have some historical email data, and FLOSSMetrics (Herraiz et al, 2009).…”

Section: Collecting the Email Archivesmentioning

confidence: 99%

How the FLOSS Research Community Uses Email Archives

Squire

2012

International Journal of Open Source Software and Processes

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Artifacts of the software development process, such as source code or emails between developers, are a frequent object of study in empirical software engineering literature. One of the hallmarks of free, libre, and open source software (FLOSS) projects is that the artifacts of the development process are publicly-accessible and therefore easily collected and studied. Thus, there is a long history in the FLOSS research community of using these artifacts to gain understanding about the phenomenon of open source software, which could then be compared to studies of software engineering more generally. This paper looks specifically at how the FLOSS research community has used email artifacts from free and open source projects. It provides a classification of the relevant literature using a publicly-available online repository of papers about FLOSS development using email. The outcome of this paper is to provide a broad overview for the software engineering and FLOSS research communities of how other researchers have used FLOSS email message artifacts in their work.

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“…We e-mailed the survey to open source software developers. Their addresses were collected from websites [6] [9]. The answers to the questionnaire were collected via a web form.…”

Section: B Experimental Setupmentioning

confidence: 99%

Analyzing the Decision Criteria of Software Developers Based on Prospect Theory

Kina

Tsunoda

Hata

et al. 2016

2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

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To enhance the quality of software, many software development support tools and software development methodologies have been proposed. However, not all proposed tools and methodologies are widely used in software development. We assume that the evaluation of tools and methodologies by developers is different from the evaluation by researchers, and that this is one of the reasons why the tools and methodologies are not widely used. We analyzed the decision criteria of software developers as applied to the tools and methodologies, to clarify whether the difference exists or not. In behavioral economics, there are theories which assume people have biases, and they do not always act reasonably. In the experiment, we made a questionnaire based on behavioral economics, and collected answers from open source software developers. The results suggest that developers do not always act to maximize expected profit because of the certainty effect and ambiguity aversion. Therefore, we should reconsider the evaluation criteria of tools such as the f-measure or AUC, which mainly focus on the expected profit.

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FLOSSmole

Cited by 102 publications

References 4 publications

Bayesian networks for enhancement of requirements engineering: a literature review

Bayesian networks for enhancement of requirements engineering: a literature review

How the FLOSS Research Community Uses Email Archives

Analyzing the Decision Criteria of Software Developers Based on Prospect Theory

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