1,720,996 research outputs found
Just-In-Time Bug Prediction in Mobile Applications: The Domain Matters!
No full textBug prediction allows developers to focus testing eorts onspecic areas of software systems. While this topic has beenextensively studied for traditional applications, investiga-tions on mobile apps are still missing. In this paper wepreliminarily study the eectiveness of a previously denedJust-In-Time bug prediction model applied onve mobileapps. Key results indicate the poor performance of the modeland the need of further research on the topic
Does source code quality reflect the ratings of apps?
No full textIn the past, bad code quality has been associated with higher bugproneness. At the same time, the main reason why mobile users negatively rate an app is due to the presence of bugs leading to crashes. In this paper, we preliminarily investigate the extent to which code quality metrics can be exploited to predict the commercial success of mobile apps. Key results suggest the existence of a relation between code quality and commercial success; We found that inheritance and information hiding metrics represent important indicators and therefore should be carefully monitored by developers
Effort-oriented methods and tools for software development and maintenance for mobile apps
No full textThe present research project aims to propose methods and tools for mobile applications development and maintenance that rely on effort information (estimations). Specifically, we will focus on two main challenges to overcome existing work: (i) conceiving effort estimation approaches that can be applied earlier in the development cycle and evolve through the development process (ii) prioritizing development and maintenance tasks by relying on effort estimation information
Illicit Darkweb Classification via Natural-language Processing: Classifying Illicit Content of Webpages based on Textual Information
No full textEnsemble techniques for software change prediction: A preliminary investigation
No full textPredicting the classes more likely to change in the future helps developers to focus on the more critical parts of a software system, with the aim of preventively improving its maintainability. The research community has devoted a lot of effort in the definition of change prediction models, i.e., models exploiting a machine learning classifier to relate a set of independent variables to the change-proneness of classes. Besides the good performances of such models, key results of previous studies highlight how classifiers tend to perform similarly even though they are able to correctly predict the change-proneness of different code elements, possibly indicating the presence of some complementarity among them. In this paper, we aim at analyzing the extent to which ensemble methodologies, i.e., machine learning techniques able to combine multiple classifiers, can improve the performances of change-prediction models. Specifically, we empirically compared the performances of three ensemble techniques (i.e., Boosting, Random Forest, and Bagging) with those of standard machine learning classifiers (i.e., Logistic Regression and Naive Bayes). The study was conducted on eight open source systems and the results showed how ensemble techniques, in some cases, perform better than standard machine learning approaches, even if the differences among them is small. This requires the need of further research aimed at devising effective methodologies to ensemble different classifiers
Not All Bugs Are the Same:Understanding, Characterizing, and Classifying Bug Types
Full text linkModern version control systems, e.g., GitHub, include bug tracking mechanisms that developers can use to highlight the presence of bugs. This is done by means of bug reports, i.e., textual descriptions reporting the problem and the steps that led to a failure. In past and recent years, the research community deeply investigated methods for easing bug triage, that is, the process of assigning the fixing of a reported bug to the most qualified developer. Nevertheless, only a few studies have reported on how to support developers in the process of understanding the type of a reported bug, which is the first and most time-consuming step to perform before assigning a bug-fix operation. In this paper, we target this problem in two ways: first, we analyze 1280 bug reports of 119 popular projects belonging to three ecosystems such as MOZILLA, APACHE, and ECLIPSE, with the aim of building a taxonomy of the types of reported bugs; then, we devise and evaluate an automated classification model able to classify reported bugs according to the defined taxonomy. As a result, we found nine main common bug types over the considered systems. Moreover, our model achieves high F-Measure and AUC-ROC (64% and 74% on overall, respectively).Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Software Engineerin
Visibility and Reputation of New Entrepreneurial Projects from Academia: the Role of Start-Up Competitions
Full text linkAcademic spin-offs, i.e., new venture projects backed by one or more researchers, are attracting increasing attention by researchers and policy makers as an effective way to increase the rate of technology transfer from Public Research Organizations (PROs) to the business environment. With the aim of supporting technology transfer processes, in the last years, many universities have carry out specific policies and a wide range of programs addressed to the development of academic entrepreneurship. Among these, the organization of a start-up competition is rather popular. But, how effective do such activities result, in particular the start-up competitions, in supporting academic researchers toward entrepreneurship? Are these kinds of initiatives able to raise the level of the reputation of academic entrepreneurial projects? If so, would such social capital have any real impact on the entrepreneurial development of academic spin-off? Is this social capital able to improve the spin-off’s ability to gain access to and acquire an initial stock of resources? Our exploratory research, following the emerging paradigm of the Quadruple-Helix Model, takes into consideration the mediating role of Media players in building visibility and reputation of nascent entrepreneurial projects from academia. The study that we performed is based on the results of web citations of business projects that won at least one prize awarded by an academic start-up competition. We consider the 2013 edition of the Italian universities business plan competitions (PNI), and we tried to measure the visibility and the reputation effect experienced by winners of local and national steps of the business plan competition. Implications of the study might be that investing in start-up competition is a useful mechanism to gain in visibility and might be useful as an ignition mechanism to start a positive entrepreneurship discourse about academic spin-offs among stakeholders that control access to valuable resources for them
Dealing With Cultural Dispersion: a Novel Theoretical Framework for Software Engineering Research and Practice
No full textSoftware development is fundamentally a team-driven process; researchers in software engineering have identified various human
and social factors that can significantly impact it. Culture emerged
as a critical element, and the diversity deriving from cultural differences can be highly impactful both positively and negatively.
Despite existing knowledge about how culture influences software
development, limitations persist. Most importantly, a unified and
comprehensive (grounded) theory of how cultural differences influence and are managed in software development has yet to exist.
This lack has two significant consequences: (1) it makes research
on culture fragmented, leading to the continual definition of new
concepts that do not allow state of the art to advance significantly,
and (2) it reduces the ability of the research to be transferred to
practitioners since there is no framework designed to be understood and used by them. To address the above-mentioned limitation,
this work proposed a theoretical framework of “Dealing With
Cultural Dispersion,” which focuses on challenges and benefits
originating from cultural differences and strategies for dealing with
them. Such a framework was developed through a qualitative study
using an iterative research approach, including interviews and
socio-technical grounded theory for data analysis. The proposed
framework was designed to reveal the tangible effects of practitioners’ culture in software development, allowing software teams to
(1) clearly understand the problem and (2) implement the correct
strategy for addressing it. Additionally, researchers can use this
framework as a foundation to (deductively) develop a more robust
and comprehensive theory in this fiel
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