Knowledge Transfer in Software Maintenance Outsourcing: The Key Roles of Software Knowledge and Guided Learning Tasks

2020 ◽  
pp. 147-181
Author(s):  
Oliver Krancher ◽  
Jens Dibbern
Keyword(s):  
Knowledge Transfer ◽  
Software Maintenance ◽  
Guided Learning ◽  
Learning Tasks ◽  
Maintenance Outsourcing

scholarly journals Offshore Software Maintenance Outsourcing: Predicting Client’s Proposal using Supervised Learning

2021 ◽  
Vol 10 (1) ◽  
pp. 106-113
Keyword(s):  
Supervised Learning ◽  
Software Maintenance ◽  
Engineering Software ◽  
Software Products ◽  
Maintenance Outsourcing ◽  
Critical Problems ◽  
Testing Accuracy ◽  
Suitable Technique ◽  
Multiple Clients ◽  
Selection Of

In software engineering, software maintenance is the process of correction, updating, and improvement of software products after handed over to the customer. Through offshore software maintenance outsourcing (OSMO) clients can get advantages like reduce cost, save time, and improve quality. In most cases, the OSMO vendor generates considerable revenue. However, the selection of an appropriate proposal among multiple clients is one of the critical problems for OSMO vendors. The purpose of this paper is to suggest an effective machine learning technique that can be used by OSMO vendors to assess or predict the OSMO client’s proposal. The dataset is generated through a survey of OSMO vendors working in a developing country. The results showed that supervised learning-based classifiers like Naïve Bayesian, SMO, Logistics apprehended 69.75 %, 81.81 %, and 87.27 % testing accuracy respectively. This study concludes that supervised learning is the most suitable technique to predict the OSMO client's proposal.

scholarly journals The Effects of Task Selection Approaches to Emphasis Manipulation on Cognitive Load and Knowledge Transfer

2021 ◽  
Vol 9 (4) ◽  
pp. 83-101
Author(s):  
Seohyun Choi ◽  
Jaewon Jung ◽  
Dongsik Kim
Keyword(s):  
Knowledge Transfer ◽  
Cognitive Load ◽  
Skill Acquisition ◽  
Transfer Test ◽  
Learning Task ◽  
Selection Strategy ◽  
Learning Context ◽  
Task Selection ◽  
Learning Tasks ◽  
The Impact

Emphasis manipulation is a way to help learners by directing their attention to particular subcomponents of a learning task. This study investigated the effects of different approaches to emphasis manipulation on knowledge transfer and cognitive load. This was done by examining the impact of three task selection strategies: system-controlled, learner-controlled, and shared-controlled. Forty-five students (n = 45) in the first or second year of high school were randomly assigned to three groups and each group used a different type of task selection to manipulate emphasis in a complex learning context. The system-controlled group carried out learning tasks that were identified as essential by the system. The learner-controlled group selected and carried out learning tasks they needed to learn. The shared-controlled group chose and carried out learning tasks that they wanted to learn from a list of suggested learning tasks. The tasks had four learning phases: pre-test, training, mental-effort rating, and transfer test. After participants completed the training, their cognitive load was measured. One week after the training, a transfer test was conducted to measure the constituent skill acquisition. The findings revealed that the system-controlled task selection strategy was the most effective in optimizing cognitive load and enhancing knowledge transfer. In addition, learners benefited from personalized guidance on learning task selection based on their expertise. Given that the shared-controlled task selection method was more effective thank the learner-controlled task selection, this study’s results indicate that learners should be provided with information about how to select learning tasks when they are allowed to do so.

scholarly journals Regularising Knowledge Transfer by Meta Functional Learning

2021 ◽  
Author(s):  
Pan Li ◽  
Yanwei Fu ◽  
Shaogang Gong
Keyword(s):  
Knowledge Transfer ◽  
Functional Model ◽  
Training Data ◽  
Functional Training ◽  
Machine Learning Classifiers ◽  
Learning Tasks ◽  
Functional Learning ◽  
Meta Learning ◽  
Update Strategy ◽  
Discriminative Functions

Machine learning classifiers’ capability is largely dependent on the scale of available training data and limited by the model overfitting in data-scarce learning tasks. To address this problem, this work proposes a novel Meta Functional Learning (MFL) by meta-learning a generalisable functional model from data-rich tasks whilst simultaneously regularising knowledge transfer to data-scarce tasks. The MFL computes meta-knowledge on functional regularisation generalisable to different learning tasks by which functional training on limited labelled data promotes more discriminative functions to be learned. Moreover, we adopt an Iterative Update strategy on MFL (MFL-IU). This improves knowledge transfer regularisation from MFL by progressively learning the functional regularisation in knowledge transfer. Experiments on three Few-Shot Learning (FSL) benchmarks (miniImageNet, CIFAR-FS and CUB) show that meta functional learning for regularisation knowledge transfer can benefit improving FSL classifiers.

Review of Giving Emphasis to Guided Learning.

1967 ◽  
Vol 12 (4) ◽  
pp. 236-236
Author(s):  
WAYNE H. HOLTZMAN
Keyword(s):  
Guided Learning
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