21 research outputs found
Talent flow analytics in online professional network
Singapore National Research Foundation under International Research Centres in Singapore Funding Initiativ
WebAPIRec: Recommending web APIs to software projects via personalized ranking
Ministry of Education, Singapore under its Academic Research Funding Tier
Information Retrieval and Spectrum Based Bug Localization: Better Together
Debugging often takes much effort and resources. To help developers debug, numerous information retrieval (IR)-based and spectrum-based bug localization techniques have been proposed. IR-based techniques process textual infor-mation in bug reports, while spectrum-based techniques pro-cess program spectra (i.e., a record of which program el-ements are executed for each test case). Both eventually generate a ranked list of program elements that are likely to contain the bug. However, these techniques only con-sider one source of information, either bug reports or pro-gram spectra, which is not optimal. To deal with the limita-tion of existing techniques, in this work, we propose a new multi-modal technique that considers both bug reports and program spectra to localize bugs. Our approach adaptively creates a bug-specific model to map a particular bug to its possible location, and introduces a novel idea of suspicious words that are highly associated to a bug. We evaluate our approach on 157 real bugs from four software systems, and compare it with a state-of-the-art IR-based bug localization method, a state-of-the-art spectrum-based bug localization method, and three state-of-the-art multi-modal feature loca-tion methods that are adapted for bug localization. Experi-ments show that our approach can outperform the baselines by at least 47.62%, 31.48%, 27.78%, and 28.80 % in terms of number of bugs successfully localized when a developer in
Network-clustered multi-modal bug localization
Developers often spend much effort and resources to debug a program. To help
the developers debug, numerous information retrieval (IR)-based and
spectrum-based bug localization techniques have been devised. IR-based
techniques process textual information in bug reports, while spectrum-based
techniques process program spectra (i.e., a record of which program elements
are executed for each test case). While both techniques ultimately generate a
ranked list of program elements that likely contain a bug, they only consider
one source of information--either bug reports or program spectra--which is not
optimal. In light of this deficiency, this paper presents a new approach dubbed
Network-clustered Multi-modal Bug Localization (NetML), which utilizes
multi-modal information from both bug reports and program spectra to localize
bugs. NetML facilitates an effective bug localization by carrying out a joint
optimization of bug localization error and clustering of both bug reports and
program elements (i.e., methods). The clustering is achieved through the
incorporation of network Lasso regularization, which incentivizes the model
parameters of similar bug reports and similar program elements to be close
together. To estimate the model parameters of both bug reports and methods,
NetML employs an adaptive learning procedure based on Newton method that
updates the parameters on a per-feature basis. Extensive experiments on 355
real bugs from seven software systems have been conducted to benchmark NetML
against various state-of-the-art localization methods. The results show that
NetML surpasses the best-performing baseline by 31.82%, 22.35%, 19.72%, and
19.24%, in terms of the number of bugs successfully localized when a developer
inspects the top 1, 5, and 10 methods and Mean Average Precision (MAP),
respectively.Comment: IEEE Transactions on Software Engineerin
PatchNet: Hierarchical deep learning-based stable patch identification for the Linux Kernel
National Research Foundation (NRF) Singapor
Talent Flow Analytics in Online Professional Network
Analyzing job hopping behavior is important for understanding job preference
and career progression of working individuals. When analyzed at the workforce
population level, job hop analysis helps to gain insights of talent flow among
different jobs and organizations. Traditionally, surveys are conducted on job
seekers and employers to study job hop behavior. Beyond surveys, job hop
behavior can also be studied in a highly scalable and timely manner using a
data driven approach in response to fast-changing job landscape. Fortunately,
the advent of online professional networks (OPNs) has made it possible to
perform a large-scale analysis of talent flow. In this paper, we present a new
data analytics framework to analyze the talent flow patterns of close to 1
million working professionals from three different countries/regions using
their publicly-accessible profiles in an established OPN. As OPN data are
originally generated for professional networking applications, our proposed
framework re-purposes the same data for a different analytics task. Prior to
performing job hop analysis, we devise a job title normalization procedure to
mitigate the amount of noise in the OPN data. We then devise several metrics to
measure the amount of work experience required to take up a job, to determine
that existence duration of the job (also known as the job age), and the
correlation between the above metric and propensity of hopping. We also study
how job hop behavior is related to job promotion/demotion. Lastly, we perform
connectivity analysis at job and organization levels to derive insights on
talent flow as well as job and organizational competitiveness.Comment: arXiv admin note: extension of arXiv:1711.05887, Data Science and
Engineering, 201