Author Archives: Raffi Khatchadourian

Slides for the “Test Dependencies and the Future of Build Acceleration” Talk

Slides for the “Test Dependencies and the Future of Build Acceleration” talk are now available on SlideShare.

Test Dependencies and the Future of Build Acceleration

Test Dependencies and the Future of Build Acceleration

JONATHAN BELL

Programming Systems Laboratory, Department of Computer Science, Columbia University

SEPTEMBER 10 @ 12:00 PM1:00 PM

With the proliferation of testing culture, many developers are facing new challenges. As projects are getting started, the focus may be on developing enough tests to maintain confidence that the code is correct. However, as developers write more and more tests, performance and repeatability become growing concerns for test suites. In our study of large open source software, we found that running tests took on average 41% of the total time needed to build each project – over 90% in those that took the longest to build. Unfortunately, typical techniques for accelerating test suites from literature (like running only a subset of tests, or running them in parallel) can’t be applied in practice safely, since tests may depend on each other. These dependencies are very hard to find and detect, posing a serious challenge to test and build acceleration. In this talk, I will present my recent research in automatically detecting and isolating these dependencies, enabling for significant, safe and sound build acceleration of up to 16x.

Jon is a fourth year PhD candidate at Columbia University studying Software Engineering with Prof Gail Kaiser. His research interests in Software Engineering mostly fall under the umbrella of Software Testing and Program Analysis. Jon’s recent research in accelerating software testing has been recognized with an ACM SIGSOFT Distinguished Paper Award (ICSE ’14), and has been the basis for an industrial collaboration with the bay-area software build acceleration company Electric Cloud. Jon actively participates in the artifact evaluation program committees of ISSTA and OOPSLA, and has served several years as the Student Volunteer chair for OOPSLA.

Gallery

Audio from “Big Data Challenges and Solutions”

In case you missed it, we now have the audio available for the talk on Big Data Challenges and Solutions from last Spring semester.

“Minimum Energy Consumption for Rate Monotonic Algorithm in a Hard Real-Time Environment” by Tin Yau Tam

Title: Minimum Energy Consumption for Rate Monotonic Algorithm in a Hard Real-Time Environment.

In cooperation with the Noyce Summer Camp

Date: June 8, 2015, 2 PM to 3 PM
Location: NAM 922A
Speaker: Prof. Tin Yau Tam (Chair of Mathematics Department at Auburn University)

Abstract: We will discuss the problem of determination of the minimum energy consumption for rate monotonic algorithm in a hard real-time environment. The solution is obtained by Lagrange Multiplier method. Because of its iterative nature, a computer algorithm is developed.

Big Data Challenges and Solutions

Computer Systems Technology Colloquium Series presents:
Big Data Challenges and Solutions
Ashwin Satyanarayana


Computer Systems Technology
New York City College of Technology
Room N906
Thursday, April 16, 2015 12-1pm
Light refreshments will be served!

Big data is set to offer tremendous insight. But with terabytes and petabytes of data pouring in to organizations today, traditional architectures and infrastructures are not up to the challenge. This begs the question: How do you present big data in a way that can be quickly understood and used? These data present tremendous opportunities in data mining, a burgeoning field in computer science that focuses on the development of methods that can extract knowledge from data. In many real world problems, data mining algorithms have access to massive amounts of data. Mining all the available data is prohibitive due to computational (time and memory) constraints. Much of the current research is concerned with scaling up data mining algorithms (i.e. improving on existing data mining algorithms for larger datasets). An alternative approach is to scale down the data. Thus, determining a smallest sufficient training set size that obtains the same accuracy as the entire available dataset remains an important research question. Our research focuses on selecting how many (sampling) instances to present to the data mining algorithm and also how to improve the quality of the data.

Dr. Ashwin Satyanarayana is an Assistant Professor in the Computer Systems Technology department at CityTech. Prior to joining CityTech, Ashwin was a Research Scientist at Microsoft, where he worked on several Big Data problems including Query Reformulation on Microsoft’s search engine Bing. Ashwin’s prior experience also includes a Senior Research Scientist on the area of Location Analytics at Placed Inc. He holds a PhD in Computer Science (Data Mining) from SUNY, with particular emphasis on Data Mining, Machine Learning and Applied Probability with applications in Real World Learning Problems.

Algorithmic Trading

Computer Systems Technology Colloquium Series presents:
Algorithmic Trading
Eugene Roumie


Computer Systems Technology
New York City College of Technology
Room N906
Thursday, April 2, 2015 12-1pm

This session will introduce Algorithmic Trading and explore the different ways it is employed by market participants, to enhance their performance using technology. It will identify the participants, the different approaches to algorithmic trading and their advantages, and will also explore the risks that are introduced as a result of these practices.

As a Quantitative Developer, Eugene specializes in developing Algorithmic Trading Strategies. He spent over 20 years on Wall Street partnering with trading desks in major banks and hedge funds to address their needs in terms of technology driven solutions, ranging from Asset Allocation to Portfolio and Risk Management. He holds an MBA in Finance from Fordham University and a BS in Computer Science from the Lebanese American University.

Poster

Slides from “Introduction to New Features in Java 8”

Slides from today’s talk on Java 8 by Raffi Khatchadourian. The slides are also available in HTML format here (sources).

Demonstration Code from Java 8 Talk

Here is the demonstration code from today’s talk on Java 8.

Introduction to New Features in Java 8

Computer Systems Technology Colloquium Series presents:
Introduction to New Features in Java 8
Raffi Khatchadourian


Computer Systems Technology
New York City College of Technology
Room N906
Thursday, March 26, 2015 12-1pm
Light refreshments will be served!

Java 8 is one of the largest upgrades to the popular language and framework in over a decade. This talk will detail several new key features of Java 8 that can help make programs easier to read, write, and maintain. Java 8 comes with many features, especially related to collection libraries. We will cover such new features as Lambda Expressions, the Stream API, enhanced interfaces, and more.

Dr. Raffi Khatchadourian is an Assistant Professor in the Department of Computer Systems Technology at New York City College of Technology of the City University of New York. He received his MS and PhD degrees in Computer Science from Ohio State University and his BS degree in Computer Science from Monmouth University, NJ. Prior to joining City Tech, he was a Software Engineer at Apple, Inc., Cupertino, California, where he worked on Digital Rights Management (DRM) for iTunes, iBooks, and the App store. He also developed distributed software that tests various features of iPhones, iPads, and iPods. His research involves automated software evolution, such as refactoring and source code recommendation systems. He is focused on easing the burden associated with correctly and efficiently evolving large and complex software by providing automated tools that can be easily used by developers.

Spring 2015 Semester Schedule Poster

Please see the attached complete Spring 2015 semester event schedule poster. This poster lists all of our events for the entire semester.

CST Colloquium Spring 2015 Schedule