(1) Degree distributions in DSNs do not always follow a power law distribution as in GSNs.
(2) Degree of separation is typically smaller in DSNs than in GSNs, and decreases over time as DSNs evolve.
(3) DSNs exhibit stronger community structures than GSNs, as measured by higher modularity values.
ALERT project aims at improving the bug resolution process in open source developers' collaborative environments by providing methods and tools based on context-aware notifications, event-driven processing and real-time interactions. This project is partially funded by European Commission under FP7.
ALERT project aims at improving the bug resolution process in open source developers' collaborative environments by providing methods and tools based on context-aware notifications, event-driven processing and real-time interactions. This project is partially funded by European Commission under FP7.
Partitioning Composite Code Changes to Facilitate Code Review (MSR2015)Sung Kim
Yida's presentation at MSR 2015!
Abstract—Developers expend significant effort on reviewing source code changes, hence the comprehensibility of code changes directly affects development productivity. Our prior study has suggested that composite code changes, which mix multiple development issues together, are typically difficult to review. Unfortunately, our manual inspection of 453 open source code changes reveals a non-trivial occurrence (up to 29%) of such composite changes.
In this paper, we propose a heuristic-based approach to automatically partition composite changes, such that each sub-change in the partition is more cohesive and self-contained. Our quantitative and qualitative evaluation results are promising in demonstrating the potential benefits of our approach for facilitating code review of composite code changes.
Developers often wonder how to implement a certain functionality
(e.g., how to parse XML files) using APIs. Obtaining
an API usage sequence based on an API-related natural
language query is very helpful in this regard. Given a query,
existing approaches utilize information retrieval models to
search for matching API sequences. These approaches treat
queries and APIs as bags-of-words and lack a deep understanding
of the semantics of the query.
We propose DeepAPI, a deep learning based approach to
generate API usage sequences for a given natural language
query. Instead of a bag-of-words assumption, it learns the
sequence of words in a query and the sequence of associated
APIs. DeepAPI adapts a neural language model named
RNN Encoder-Decoder. It encodes a word sequence (user
query) into a fixed-length context vector, and generates an
API sequence based on the context vector. We also augment
the RNN Encoder-Decoder by considering the importance
of individual APIs. We empirically evaluate our approach
with more than 7 million annotated code snippets collected
from GitHub. The results show that our approach generates
largely accurate API sequences and outperforms the related
approaches.
Defect, defect, defect: PROMISE 2012 Keynote Sung Kim
Software prediction leveraging repositories has received a tremendous amount of attention within the software engineering community, including PROMISE. In this talk, I will first present great achievements in defect prediction research including new defect prediction features, promising algorithms, and interesting analysis results. However, there are still many challenges in defect prediction. I will talk about them and discuss potential solutions for them leveraging prediction 2.0.
Partitioning Composite Code Changes to Facilitate Code Review (MSR2015)Sung Kim
Yida's presentation at MSR 2015!
Abstract—Developers expend significant effort on reviewing source code changes, hence the comprehensibility of code changes directly affects development productivity. Our prior study has suggested that composite code changes, which mix multiple development issues together, are typically difficult to review. Unfortunately, our manual inspection of 453 open source code changes reveals a non-trivial occurrence (up to 29%) of such composite changes.
In this paper, we propose a heuristic-based approach to automatically partition composite changes, such that each sub-change in the partition is more cohesive and self-contained. Our quantitative and qualitative evaluation results are promising in demonstrating the potential benefits of our approach for facilitating code review of composite code changes.
Developers often wonder how to implement a certain functionality
(e.g., how to parse XML files) using APIs. Obtaining
an API usage sequence based on an API-related natural
language query is very helpful in this regard. Given a query,
existing approaches utilize information retrieval models to
search for matching API sequences. These approaches treat
queries and APIs as bags-of-words and lack a deep understanding
of the semantics of the query.
We propose DeepAPI, a deep learning based approach to
generate API usage sequences for a given natural language
query. Instead of a bag-of-words assumption, it learns the
sequence of words in a query and the sequence of associated
APIs. DeepAPI adapts a neural language model named
RNN Encoder-Decoder. It encodes a word sequence (user
query) into a fixed-length context vector, and generates an
API sequence based on the context vector. We also augment
the RNN Encoder-Decoder by considering the importance
of individual APIs. We empirically evaluate our approach
with more than 7 million annotated code snippets collected
from GitHub. The results show that our approach generates
largely accurate API sequences and outperforms the related
approaches.
Defect, defect, defect: PROMISE 2012 Keynote Sung Kim
Software prediction leveraging repositories has received a tremendous amount of attention within the software engineering community, including PROMISE. In this talk, I will first present great achievements in defect prediction research including new defect prediction features, promising algorithms, and interesting analysis results. However, there are still many challenges in defect prediction. I will talk about them and discuss potential solutions for them leveraging prediction 2.0.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Assuring Contact Center Experiences for Your Customers With ThousandEyes
The Anatomy of Developer Social Networks
1. The Anatomy of Developer Social
Networks
Qiaona HONG
Supervisor: Prof. Shing-Chi Cheung
1
2. Social Network
• Study the Topological Structure of Social
Network
– Y. Y. Ahn @WWW '07; A. Mislove@IMC '07
• Study the Community Structure of Social
Network
– V. D. Blondel@ Journal of Statistical Mechanics:
Theory and Experiment; Y. R. Lin@WI '07
• Techniques to visualize the social network
– Jeffrey Heer@InfoVis '05
• Influential People & Information Diffusion
General Social Network – Kimura, M.@InfoVis '07
(GSN) • Friend Recommendation
– Nitai B. Silva@WCCI‘10
2
4. Research Questions
• Q1: What are the similarities and differences
between DSNs and GSNs?
• Q2: How do DSNs evolve over time?
• Q3: How do communities evolve in DSNs?
• Q4: What are the similarities and differences
between DSNs extracted using different social
linkage indicators?
4
5. Research Questions
• Q1: What are the similarities and differences
between DSNs and GSNs?
• Q2: How do DSNs evolve over time?
• Q3: How do communities evolve in DSNs?
•Qiaona HONG, the similarities and differences
Q4: What are Sunghun Kim, S.C. Cheung and
Christian Bird, “Understanding a different social
between DSNs extracted using Developer Social
Network indicators?
linkage and its Evolution”, in Proceedings of the
27th IEEE International Conference on Software
Maintenance, 2011.
5
7. DSN Extraction Approach
Bug Report 1 Bug Report 2 Bug Report 3 Bug Report 4
David Comment 1
David Comment 1 Bob Comment 1 Bob Comment 2
Bob Comment 2
Bob Comment 2 Jack Comment 2 Jack Comment 3 Jack Comment 3
Jack Comment 3 Bill Comment 3 Bill Comment 3
David Bill
Bob Jack 7
8. DSN Extraction Approach
Bug Report 1 Bug Report 2 Bug Report 3 Bug Report 4
David Comment 1
David Comment 1 Bob Comment 1 Bob Comment 2
Bob Comment 2
Bob Comment 2 Jack Comment 2 Jack Comment 3 Jack Comment 3
Jack Comment 3 Bill Comment 3 Bill Comment 3
1
David Bill
2 2
2 2
4
Bob Jack 8
9. DSN Extraction Approach
Bug Report 1 Bug Report 2 Bug Report 3 Bug Report 4
David Comment 1
David Comment 1 Bob Comment 1 Bob Comment 2
Bob Comment 2
Bob Comment 2 Jack Comment 2 Jack Comment 3 Jack Comment 3
Jack Comment 3 Bill Comment 3 Bill Comment 3
David Bill
4
Bob Jack 9
10. DSN Extraction Approach
Bug Report 1 Bug Report 2 Bug Report 3 Bug Report 4
David Comment 1
David Comment 1 Bob Comment 1 Bob Comment 2
Bob Comment 2
Bob Comment 2 Jack Comment 2 Jack Comment 3 Jack Comment 3
Jack Comment 3 Bill Comment 3 Bill Comment 3
Bob Jack
10
11. Metrics
• Degree Distribution
– The number of edges connected to a node
• Degree of Separation
– The shortest path between two nodes
• Modularity
– To measure the quality of division of nodes
• Community Size
– The number of nodes within a community
11
12. Modularity
A 0.51 B 0.176
• According to A. Clauset’s work, modularity of 0.3 is
a good indicator of significant community structure
in a network
• When the modularity is 0, the community structure
is no stronger than that of a randomly generated
network 12
13. Communities in DSN
• Identified Communities in DSN
– Louvain Algorithm (by optimizing modularity)
– 50 different input ordering of nodes
13
14. ?
Q1: What are the similarities
and differences between
DSNs and GSNs
Degree of Distribution Degree of Separation
Modularity Community Size
14
15. Q1: What are the similarities and differences between DSNs and GSNs
Degree Distribution
(1) MozillaDSN-BR (2) MozillaDSN-CL
(3) EclipseDSN-BR (4) EclipseDSN-CL
15
16. Q1: What are the similarities and differences between DSNs and GSNs
Degree Distribution
(1) MozillaDSN-BR (2) MozillaDSN-CL
(3) EclipseDSN-BR (4) EclipseDSN-CL
16
17. Q1: What are the similarities and differences between DSNs and GSNs
Degree Distribution
• Quantitative power law fit test
– An approach of analyzing power law distributed
data introduced by A. Clauset et al.
• P-value : The likelihood that(2) MozillaDSN-CL
(1) MozillaDSN-BR degree
distribution does actually follow a power-law
– If p-value is less than 0.1, the power law is
rejected.
(3) EclipseDSN-BR (4) EclipseDSN-CL
17
18. Q1: What are the similarities and differences between DSNs and GSNs
P-value<0.1
Degree some<0.1,other>0.1
Distribution
(1) MozillaDSN-BR (2) MozillaDSN-CL
Different from GSNs, DSNs do not(4) EclipseDSN-CL
(3) EclipseDSN-BR follow power-law
18
22. Q1: What are the similarities and differences between DSNs and GSNs
Modularity Modularity
MozillaDSN-CL
0.7
0.6
0.5
0.4
0.3
MozillaDSN-BR
0.7
0.6
0.5
0.4
Modularity
0.3
EclipseDSN-CL
0.7
0.6
0.5
0.4
0.3
EclipseDSN-BR
0.7
0.6
0.5
0.4
0.3
ok
SN
SN
SN
rld
N
N
N
DS
DS
DS
bo
wo
D
D
D
ce
th
th
th
ar
ar
ar
Cy
on
on
on
Fa
ye
ye
ye
m
m
m
1-
2-
4-
1-
3-
6-
Network
Similar to GSNs, all DSNs have significant community structure
22
23. Q1: What are the similarities and differences between DSNs and GSNs
Community Size
(1) MozillaDSN-BR (2) MozillaDSN-CL
(3) EclipseDSN-BR (4) EclipseDSN-CL
23
24. Q1: What are the similarities and differences between DSNs and GSNs
Community Size
28%
(1) MozillaDSN-BR (2) MozillaDSN-CL
(3) EclipseDSN-BR (4) EclipseDSN-CL
24
25. Q1: What are the similarities and differences between DSNs and GSNs
Community Size
21%-36% 23%-43%
(1) MozillaDSN-BR (2) MozillaDSN-CL
15%-30% 23%-33%
(3) EclipseDSN-BR (4) EclipseDSN-CL
25
26. ?
Q4:What are the similarities and
differences between DSNs extracted
using different social linkage indicators
Q2: How do DSNs evolve over time?
Degree of Distribution Degree of Separation
Modularity Community Size
26
27. Q2: How do DSNs evolve over time?
Change of Developer Size
DSNs-BR always have more developers than DSNs-CL
27
28. Q2: How do DSNs evolve over time?
Change of Percentage of New Comers
DSNs-BR always have higher percentage of new
comers than DSNs-CL
28
Editor's Notes
Metrics to analyze the social networkTechniques to visualize the social networkFinding influential peopleFinding communityInformation diffusionRecommendationStudy the Topological Structure of Social Network[1] Y. Y. Ahn, S. Han, H. Kwak, S. Moon, and H. Jeong, "Analysis of topological characteristics of huge online social networking services," in WWW '07: Proceedings of the 16th international conference on World Wide Web. New York, NY, USA: ACM, 2007, pp. 835-844.[2] A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee, "Measurement and analysis of online social networks," in Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, ser. IMC '07. New York, NY, USA: ACM, 2007, pp. 29-42.Study the Community Structure of Social Network[1] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, "Fast unfolding of communities in large networks," Journal of Statistical Mechanics: Theory and Experiment, vol. 2008, no. 10, pp. P10 008+, Jul. 2008.[2] Y. R. Lin, H. Sundaram, Y. Chi, J. Tatemura, and B. L. Tseng, "Blog community discovery and evolution based on mutual awareness expansion," in WI '07: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence. Washington, DC, USA: IEEE Computer Society, 2007, pp. 48-56.Study the Topological Structure of Social NetworkDegree distribution [Y. Y. Ahn @WWW '07]Power-law, small-world [A. Mislove@IMC '07]Study the Community Structure of Social NetworkCommunity structure extraction method[V. D. Blondel@ Journal of Statistical Mechanics: Theory and Experiment]Evolution of community, community evolution patterns [Y. R. Lin@WI '07]Techniques to visualize the social networkCommunity structure extraction method[V. D. Blondel@ Journal of Statistical Mechanics: Theory and Experiment]Finding Influential PeopleCommunity structure extraction method[V. D. Blondel@ Journal of Statistical Mechanics: Theory and Experiment]Information DiffusionCommunity structure extraction method[V. D. Blondel@ Journal of Statistical Mechanics: Theory and Experiment]
A nature question to ask here is that ..
Apart from Q1, in this thesis, we also study other research question, cite my paper here [very important]
Apart from Q1, in this thesis, we also study other research question, cite my paper here [very important]
The subjects used for this study are Mozilla Bug Report, Mozilla CVS Log, Eclipse Bug Report, Eclipse CVS Log.Both Mozilla and Eclipse are very successful open source projects.To compare with GSN, we extract DSNs from these two projects.
Why I used these metrics? I need to polish this slide by using more formal sentences.
[8] A. Clauset, M. E. J. Newman, and C. Moore, "Finding community structure in very large networks," Aug. 2004.
BOF meetings. Developer are free to join the BOF meetings. So we consider BOF meetings reflect real communities.One identified community may contain more than one BOF meetings. However one BOF only be contained in one identified community.Which means BOF represent finer division of developers and Our identified communities reflect real communities.
Why this question? There are many possibilities. Please list some here.
To compare with GSN, we extract DSN from different length of time 1-month, 3-month, 6-month, 1-year, 2-year ,4-years. Possible result, my effort is not trivial.How to interpret the graph.
To compare with GSN, we extract DSN from different length of time 1-month, 3-month, 6-month, 1-year, 2-year ,4-years. Possible result, my effort is not trivial.
To compare with GSN, we extract DSN from different length of time 1-month, 3-month, 6-month, 1-year, 2-year ,4-years. Possible result, my effort is not trivial.
To compare with GSN, we extract DSN from different length of time 1-month, 3-month, 6-month, 1-year, 2-year ,4-years.
I need more text on the slides
28%
28%
This is a GREAT slide. Be sure to explain Extinct and Emerge well since both has “empty” on one side of the arrow.
In the paper, we examine the community evolution from 2000 to 2009, here we use the period from 2005 to 2009 to illustrate our findings.This is also a very good slide. I like the tracking of different paths of communities over time.
In the paper, we examine the community evolution from 2000 to 2009, here we use the period from 2005 to 2009 to illustrate our findings.This is also a very good slide. I like the tracking of different paths of communities over time.
In the paper, we examine the community evolution from 2000 to 2009, here we use the period from 2005 to 2009 to illustrate our findings
[1] Xin Yang, RaulaGaikovina Kula, Camargo Cruz Ana Erika, Norihiro Yoshida, KazukiHamasaki, Kenji Fujiwara, and Hajimu Iida, "Understanding OSS Peer Review Roles in Peer Review Social Network (PeRSoN)," In Proceedings of the 19th Asia-Pacific Software Engineering Conference (APSEC2012), (to appear)
Xin Yang in their work, they used our approach for peer review system to generate a peer review social networks. Based on this review social networks, they target to investigate the importance of OSS peer review contributor roles and their review activities.JifengXuan, He Jiang, ZhileiRen, WeiqinZou, “Developer Prioritization in Bug Repositories”, In Proceedings of the 34th International Conference on Software Engineering (ICSE 2012), pp. 25-35, 2012. Y. Tian, P. Achananuparp, I. Lubis, D. Lo, and E.-P. Lim. What does software engineering community microblog about? In MSR, 2012.To investigate the importance of OSS peer-review contributers and review activities.
Xin Yang in their work, they used our approach for peer review system to generate a peer review social networks. Based on this review social networks, they target to investigate the importance of OSS peer review contributor roles and their review activities.JifengXuan, He Jiang, ZhileiRen, WeiqinZou, “Developer Prioritization in Bug Repositories”, In Proceedings of the 34th International Conference on Software Engineering (ICSE 2012), pp. 25-35, 2012. Y. Tian, P. Achananuparp, I. Lubis, D. Lo, and E.-P. Lim. What does software engineering community microblog about? In MSR, 2012.To investigate the importance of OSS peer-review contributers and review activities.
files are likely to be vulnerable when changed by many developers who have made many changes to other files. Practitioners can use these observations to prioritize securi