Indian Institute of Technology Hyderabad (IITH) is a premier institute of science and technology established in 2008. IITH has been consistently ranked in the&nbsp;<a href="https://iith.ac.in/reports/" rel="noopener noreferrer">top 10 institutes</a>&nbsp;in India for Engineering&nbsp;<a href="https://www.nirfindia.org/2021/EngineeringRanking.html" rel="noopener noreferrer" target="_blank">according to NIRF</a>&nbsp;making it one of the most coveted schools for science and technology in the country.

IITH offers&nbsp;<a href="https://iith.ac.in/academics/under-graduate/">undergraduate programs</a>&nbsp;in all the classical engineering disciplines, applied sciences, design, as well as several modern interdisciplinary areas. Students are given a flexibility to explore a broad set of areas, and potentially pursue a minor or double major in a discipline that is not their own. Students who wish to seek a deeper understanding of their own discipline are strongly encouraged to get involved in cutting-edge research with the help of a faculty to mentor them, and earn an Honors in their own field.

The very foundation of IIT Hyderabad is based on&nbsp;<a href="https://iith.ac.in/research/">research and innovation</a>. The vibrant research culture is evident from the number of patents and publications that IITH produces consistently every year. IITH offers&nbsp;<a href="https://iith.ac.in/academics/programmes-offered/">graduate programs</a>&nbsp;at both a masters, and a doctoral level, in several diverse areas. There are separate programs for technology, design, science, and liberal arts. The&nbsp;<a href="https://iith.ac.in/academics/post-graduate/">MTech program</a>&nbsp;is offered in various modes and durations to cater to the ever-growing need of postgraduate level professionals in the country.

IITH encourages and supports&nbsp;<a href="https://iic-iith.netlify.app/index.html" rel="noopener noreferrer" target="_blank">innovation and entrepreneurship</a>&nbsp;at every stage. The minor program in entrepreneurship is open to all students of IITH, and many of the courses are offered by industrialists who are entrepreneurs themselves. There are various&nbsp;<a href="https://iith.ac.in/research/centres-incubators/">innovation centers and incubators</a>&nbsp;that promote entrepreneurship and help mentor young innovators. IITH has been very successful in building tie-ups with leading academic institutions around the globe.

IITH enjoys a very special relationship with Japanese universities and industries that goes beyond academic and research collaborations. In fact, some of the iconic buildings in IITH campus will carry the signature of Japanese architecture.

IITH creates a unique holistic ecosystem for education that offers interactive learning, a very flexible academic structure, cutting-edge research, strong industry collaboration, and entrepreneurship. This is an environment which enables students and faculty to translate their dreams into realities.

IIT Hyderabad

Modern applications, such as social networking systems and e-commerce platforms are centered around using large-scale storage systems for storing and retrieving data. In the presence of concurrent accesses, these storage systems trade off isolation for performance. The weaker the isolation level, the more behaviors a storage system is allowed to exhibit and it is up to the developer to ensure that their application can tolerate those behaviors. However, these weak behaviors only occur rarely in practice and outside the control of the application, making it difficult for developers to test the robustness of their code against weak isolation levels. This paper presents MonkeyDB, a mock storage system for testing storage-backed applications. MonkeyDB supports a key-value interface as well as SQL queries under multiple isolation levels. It uses a logical specification of the isolation level to compute, on a read operation, the set of all possible return values. MonkeyDB then returns a value randomly from this set. We show that MonkeyDB provides good coverage of weak behaviors, which is complete in the limit. We test a variety of applications for assertions that fail only under weak isolation. MonkeyDB is able to break each of those assertions in a small number of attempts. © 2021 Owner/Author.

World University root node

Department of Computer Science and Engineering

Proceedings of the ACM on Programming Languages

MonkeyDB: Effectively testing correctness under weak isolation levels

Testing concurrent systems remains an uncomfortable problem for developers. The common industrial practice is to stress-test a system against large workloads, with the hope of triggering enough corner-case interleavings that reveal bugs. However, stress testing is often inefficient and its ability to get coverage of interleavings is unclear. In reaction, the research community has proposed the idea of systematic testing, where a tool takes over the scheduling of concurrent actions so that it can perform an algorithmic search over the space of interleavings.We present an experience paper on the application of systematic testing to several case studies. We separate the algorithmic advancements in prior work (on searching the large space of interleavings) from the engineering of their tools. The latter was unsatisfactory; often the tools were limited to a small domain, hard to maintain, and hard to extend to other domains. We designed Nekara, an open-source cross-platform library for easily building custom systematic testing solutions.We show that (1) Nekara can effectively encapsulate state-of-the-art exploration algorithms by evaluating on prior bench-marks, and (2) Nekara can be applied to a wide variety of scenarios, including existing open-source systems as well as production distributed services of Microsoft Azure. Nekara was easy to use, improved testing, and found multiple new bugs. © 2021 IEEE.

Proceedings - 2021 36th IEEE/ACM International Conference on Automated Software Engineering, ASE 2021

Nekara: Generalized Concurrency Testing

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Preface

Many program-analysis based tools require precise points-to/alias information only for some program variables. To meet this requirement efficiently, there have been many works on demand-driven analyses that perform only the work necessary to compute the points-to or alias information on the requested variables (queries). However, these demand-driven analyses can be very expensive when applied on large systems where the number of queries can be significant. Such a blow-up in analysis time is unacceptable in cases where scalability with real-time constraints is crucial; for example, when program analysis tools are plugged into an IDE (Integrated Development Environment). In this paper, we propose schemes to improve the scalability of demand-driven analyses without compromising on precision. Our work is based on novel ideas for eliminating irrelevant and redundant data-flow paths for the given queries. We introduce the idea of batch analysis, which can answer multiple given queries in batch mode. Batch analysis suits the environments with strict time constraints, where the queries come in batch. We present a batch alias analysis framework that can be used to speed up given demand-driven alias analysis. To show the effectiveness of this framework, we use two demand-driven alias analyses (1) the existing best performing demand-driven alias analysis tool for race-detection clients and (2) an optimized version thereof that avoids irrelevant computation. Our evaluations on a simulated data-race client, and on a recent program-understanding tool, show that batch analysis leads to significant performance gains, along with minor gains in precision. © 2019 IEEE.

Proceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019

Batch alias analysis

This paper presents a Tensor Transposition Library for GPUs (TTLG). A distinguishing feature of TTLG is that it also includes a performance prediction model, which can be used by higher level optimizers that use tensor transposition. For example, tensor contractions are often implemented by using the TTGT (Transpose-Transpose-GEMM-Transpose) approach-transpose input tensors to a suitable layout and then use high-performance matrix multiplication followed by transposition of the result. The performance model is also used internally by TTLG for choosing among alternative kernels and/or slicing/blocking parameters for the transposition. TTLG is compared with current state-of-The-Art alternatives for GPUs. Comparable or better transposition times for the 'repeated-use' scenario and considerably better 'single-use' performance are observed. © 2018 IEEE.

Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018

Ttlg-an efficient tensor transposition library for GPUs

Code with Subclass and State JYOTHI VEDURADA, IIT Madras, India V. KRISHNA NANDIVADA, IIT Madras, India 138 Refactoring is a program transformation that restructures existing code without altering its behaviour and is a key practice in popular software design movements, such as Agile. Identification of potential refactoring opportunities is an important step in the refactoring process. In large systems, manual identification of useful refactoring opportunities requires a lot of effort and time. Hence, there is a need for automatic identification of refactoring opportunities. However, this problem has not been addressed well for many non-trivial refactorings. Two such non-trivial, yet popular refactorings are łReplace Type Code with Subclass" (SC) and łReplace Type Code with State" (ST) refactorings. In this paper, we present new approaches to identify SC and ST refactoring opportunities. Our proposed approach is based around the notion of control-fields. A control-field is a field of a class that exposes the different underlying behaviors of the class. Each control-field can lead to a possible SC/ST refactoring of the associated/interacting classes. We first present a formal definition of control-fields and then present algorithms to identify and prune them; each of these pruned control-fields represents a refactoring opportunity. Further, we present a novel flow- and context-sensitive analysis to classify each of these refactoring opportunities into one of the SC and ST opportunities. We have implemented our proposed approach in a tool called Auto-SCST, and demonstrated its effectiveness by evaluating it against eight open-source Java applications. © 2018 Copyright held by the owner/author(s).

Identifying refactoring opportunities for replacing type code with subclass and state

Refactoring restructures a program to improve itsreadability and maintainability, without changing its originalbehavior. One of the key steps in refactoring is the identification ofpotential refactoring opportunities. In this paper, we discuss therelevance of two popular refactorings 'Replace Type Code withSubclass' and 'Replace Type Code with State' in real world Javaapplications and describe some of the challenges in automaticallyidentifying these refactoring opportunities. © 2017 IEEE.