Amey Karkare

Chavhan Sujeet Yashavant, Saurabh Kumar, Amey Karkare

Smart contracts on Ethereum handle millions of U.S. Dollars and other financial assets. In the past, attackers have exploited smart contracts to steal these assets. The Ethereum community has developed plenty of tools to detect vulnerable smart contracts. However, there is no standardized data set to evaluate these existing tools, or any new tools developed. There is a need for an unbiased standard benchmark of real-world Ethereum smart contracts. We have created ScrawlD: an annotated data set of real-world smart contracts taken from the Ethereum network. The data set is labelled using 5 tools that detect various vulnerabilities in smart contracts, using majority voting.

Umair Z. Ahmed, Renuka Sindhgatta, Nisheeth Srivastava et al.

We present TEGCER, an automated feedback tool for novice programmers. TEGCER uses supervised classification to match compilation errors in new code submissions with relevant pre-existing errors, submitted by other students before. The dense neural network used to perform this classification task is trained on 15000+ error-repair code examples. The proposed model yields a test set classification Pred@3 accuracy of 97.7% across 212 error category labels. Using this model as its base, TEGCER presents students with the closest relevant examples of solutions for their specific error on demand.

Pawan Kr Patel, Amey Karkare

Computer-based tests (CBTs) play an important role in the professional career of any person. Universities use CBTs for admissions. Further, many large courses use CBTs for evaluation and grading. Almost all software companies use CBTs to offer jobs. However, many of these CBTs do not take attention to the accessibility barriers for persons with disabilities, specifically visually impaired persons. In this paper, we present a study of accessibility barriers in various CBTs as faced by visually impaired persons in India. These barriers have been identified by a questionnaire survey approach. Our analysis of the responses shows that most CBTs do not meet the expectations of visually impaired persons. We conclude the paper with some recommendations to improve accessibility.

Saksham Sharma, Pallav Agarwal, Parv Mor et al.

With the widespread adoption of MOOCs in academic institutions, it has become imperative to come up with better techniques to solve the tutoring and grading problems posed by programming courses. Programming being the new 'writing', it becomes a challenge to ensure that a large section of the society is exposed to programming. Due to the gradient in learning abilities of students, the course instructor must ensure that everyone can cope up with the material, and receive adequate help in completing assignments while learning along the way. We introduce TipsC for this task. By analyzing a large number of correct submissions, TipsC can search for correct codes resembling a given incorrect solution. Without revealing the actual code, TipsC then suggests changes in the incorrect code to help the student fix logical runtime errors. In addition, this also serves as a cluster visualization tool for the instructor, revealing different patterns in user submissions. We evaluated the effectiveness of TipsC's clustering algorithm on data collected from previous offerings of an introductory programming course conducted at IIT Kanpur where the grades were given by human TAs. The results show the weighted average variance of marks for clusters when similar submissions are grouped together is 47% less compared to the case when all programs are grouped together.

Rajdeep Das, Umair Z. Ahmed, Amey Karkare et al.

An introductory programming course (CS1) is an integral part of any undergraduate curriculum. Due to large number and diverse programming background of students, providing timely and personalised feedback to individual students is a challenging task for any CS1 instructor. The help provided by teaching assistants (typically senior students) is not sufficient as it suffers from unintentional bias and, most of the time, not quick enough. In this paper, we present Prutor, a tutoring system platform to conduct introductory programming courses. Prutor is a cloud-based web application that provides instant and useful feedback to students while solving programming problems. Prutor stores, at regular intervals, the snapshots of students' attempts to solve programming problems. These intermediate versions of the student programs provide the instructors (and data analysts) a view of the students' approach to solving programming problems. Since Prutor is accessible through any standard web browser, students do not need to worry about dependencies external to the programming course, viz. Operating Systems, Editors, Compilers, Compiler Options, etc.. This enables the students to focus on solving only the programming problems. Apart from the code snapshots at regular intervals, Prutor also collects other valuable data such as the time taken by the students to solve the problems, the number of compile and execution events, and the errors made. We have used this data in developing intelligent tools for giving feedback to students, some of which are described briefly in this paper. This system thus serves as a platform for tutoring as well as data collection for researchers.

Saravana Perumal P, Amey Karkare

Porting GCC to new architecture requires writing a Machine Description (MD) file that contains mapping from GCC's intermediate form to the target assembly code. Constructing an MD file is a difficult task because it requires the user to understand both (a) the internals of GCC, and (b) the intricacies of the target architecture. Instruction sets of different architectures exhibit significant amount of semantic similarities across a large class (for example, the instruction sets for RISC architectures) and differ only in syntax. Therefore, it is expected that MD files of machines with similar architectures should also have similarities. To confirm our hypothesis, we created "mdcompare", a tool to (a) extract RTL patterns (machine independent abstraction of RTL templates) from MD files of well known architectures and (b) compare the similarity of patterns across architectures. The results are encouraging; we found that 28% -- 70% RTL expressions are similar across pairs of MD files, the similarity percentage being on the higher side for pairs of similar architectures.