Upcoming talks

Titles:  

(1)  Automated Black-box API Testing, by Diptikalyan Saha

(2)  Symbolic Execution in Testing Industrial Applications, by Sujit Kumar Chakrabarti

Time: 18 April 2023, 19:00 - 20:00 India Standard Time, by Zoom

Host: Komondoor V Raghavan, IISc Bangalore

Registration: Use this link to register for the talk.  Registration is required to attend the talk, which takes only 1 minute. Register any time before the talk, but register early to ensure your spot! After registering, you will receive a confirmation email containing information about joining the meeting. 

Abstracts 

(1) The proliferation of web-based applications has increased the need for the testing of web services. With the adoption of web service standards such as REST (Representational State Transfer) and SOAP (Simple Object Access Protocol), it has become easier for developers to build and consume APIs. The testing of REST APIs has been a topic of interest in the recent past. However, these studies mostly focus on the testing of REST APIs with the aim of finding bugs in the system under test. Functional testing involves testing the functional behavior of the system under test. In this work, we aim to automatically generate realistic functional test cases which can even be used for regression. Functional testing seeks to cover valid functional scenarios, the notion we concretely define and present an algorithm to generate nominal/valid test cases following a functional sequence of operations. We used a resource-based grouping strategy, a novel producer-consumer dependency inference algorithm, and a language model-based sequencing algorithm to generate an operational sequence suitable for functional test cases.

(2)  We discuss our recent work on using symbolic execution for generating test inputs for industrial embedded systems. We consider a specific class of applications we term as acyclic core applications, a control flow structure that represents a fairly large number of embedded applications. Our goal is not to achieve full coverage but to reach specific parts of the control flow graph as early as possible. Our symbolic execution approach proceeds along a single path at a time and backtracks on meeting infeasibility always trying to optimise the path so that the targets are covered early. We discuss the basic framework, and then present two improvements on that, primarily to do good backtracking. The first method is based on probabilistic calculations based on past runs. The second method is based on a Q table based learning technique akin to reinforcement learning.

Speaker biographies:   

(1)  Dr. Diptikalyan Saha is Sr. Technical Staff Member at IBM Research Lab in India. His current research focuses on AI Testing where he is trying to ensure Trustworthy AI by developing novel techniques to test, debug and repair AI models and applying AI techniques to improve traditional software testing. Dipti holds a Ph.D. in Computer Science from the State University of New York at Stony Brook. He is a master inventor, an ACM senior member, and currently co-chairing ESEC-FSE'23 Industry track. 

(2)  Sujit Kumar Chakrabarti has a Ph.D. from the department of Computer Science and Automation, Indian Institute of Science, Bangalore. Prior to that he has a masters from University of Roorkee (now IIT Roorkee) in measurement and instrumentation, and a BE in electrical engineering from Nagpur University. His research interests are centred around software engineering, formal methods, programming languages, education technology and software testing. Sujit has nearly 8 years of experience in the industry, with companies like Tata Consultancy Services, Philips and General Motors both in research and software development. Prior to joining IIITB, Sujit was with Jed-i, an educational startup. Sujit's technical interests are in programming, engineering, and tech-blogging. He also has an irrepressible artistic side being a cartoonist of fair calibre and a flair for sketching and water colours. He is also an avid blogger.