Impacts of knowledge expectations on recipients’ continuous cross-project learning intention

2021 ◽  
Author(s):  
Dali Zhao ◽  
Yuan Jiang ◽  
Chunpei Lin ◽  
Xianyue Liu ◽  
Yenchun Jim Wu
Keyword(s):  
Project Learning ◽  
Learning Intention ◽  
Cross Project

Enhancing Cross-Project Learning

2004 ◽  
Vol 16 (1) ◽  
pp. 12-20 ◽  
Author(s):  
Sue Newell
Keyword(s):  
Project Learning ◽  
Cross Project

scholarly journals A public unified bug dataset for java and its assessment regarding metrics and bug prediction

2020 ◽  
Vol 28 (4) ◽  
pp. 1447-1506 ◽  
Author(s):  
Rudolf Ferenc ◽  
Zoltán Tóth ◽  
Gergely Ladányi ◽  
István Siket ◽  
Tibor Gyimóthy
Keyword(s):  
Prediction Models ◽  
Source Code ◽  
Decision Tree Algorithm ◽  
Large Dataset ◽  
Code Analysis ◽  
Project Learning ◽  
Code Metrics ◽  
Public Datasets ◽  
Source Code Metrics ◽  
Cross Project

AbstractBug datasets have been created and used by many researchers to build and validate novel bug prediction models. In this work, our aim is to collect existing public source code metric-based bug datasets and unify their contents. Furthermore, we wish to assess the plethora of collected metrics and the capabilities of the unified bug dataset in bug prediction. We considered 5 public datasets and we downloaded the corresponding source code for each system in the datasets and performed source code analysis to obtain a common set of source code metrics. This way, we produced a unified bug dataset at class and file level as well. We investigated the diversion of metric definitions and values of the different bug datasets. Finally, we used a decision tree algorithm to show the capabilities of the dataset in bug prediction. We found that there are statistically significant differences in the values of the original and the newly calculated metrics; furthermore, notations and definitions can severely differ. We compared the bug prediction capabilities of the original and the extended metric suites (within-project learning). Afterwards, we merged all classes (and files) into one large dataset which consists of 47,618 elements (43,744 for files) and we evaluated the bug prediction model build on this large dataset as well. Finally, we also investigated cross-project capabilities of the bug prediction models and datasets. We made the unified dataset publicly available for everyone. By using a public unified dataset as an input for different bug prediction related investigations, researchers can make their studies reproducible, thus able to be validated and verified.

A THEORETICAL FRAMEWORK FOR UNDERSTANDING THE ORGANISATION OF THE R&D FUNCTION: AN EMPIRICAL ILLUSTRATION FROM THE CHEMICAL AND PHARMACEUTICAL INDUSTRY

2006 ◽  
Vol 10 (04) ◽  
pp. 455-476 ◽  
Author(s):  
FLORENCE CHARUE-DUBOC
Keyword(s):  
Pharmaceutical Industry ◽  
Mergers And Acquisitions ◽  
Absorptive Capacity ◽  
Theoretical Framework ◽  
Innovation Performance ◽  
Project Learning ◽  
Research Function ◽  
Economy Of Scope ◽  
Empirical Illustration ◽  
Cross Project

The increasing importance of innovation for companies, mergers and acquisitions, and the strengthening of project structures are leading to numerous rationalisations in the organisation of the research function. Although few works have analysed company R&D organisation and its impact on innovation performance, we elaborate on the concepts of economy of scope and absorptive capacity, introduced to compare the efficiency of various firms' R&D, so as to analyse the organisation of R&D departments. We focus on inter-project learning and argue that it must be viewed as an organisational question. "Organising by problem" constitutes a new means of cross-project learning and of enhancing absorptive capacity.

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