arXiv subjects

Sumit Gulwani

Publications and source records attributed to Sumit Gulwani.

82 records · Page 5Linked to original sources

arXiv2016

Prutor: A System for Tutoring CS1 and Collecting Student Programs for Analysis

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.

cs.CY↗

arXiv2016

Semi-Supervised Verified Feedback Generation

Students have enthusiastically taken to online programming lessons and contests. Unfortunately, they tend to struggle due to lack of personalized feedback when they make mistakes. The overwhelming number of submissions precludes manual evaluation. There is an urgent need of program analysis and repair techniques capable of handling both the scale and variations in student submissions, while ensuring quality of feedback. Towards this goal, we present a novel methodology called semi-supervised verified feedback generation. We cluster submissions by solution strategy and ask the instructor to identify or add a correct submission in each cluster. We then verify every submission in a cluster against the instructor-validated submission in the same cluster. If faults are detected in the submission then feedback suggesting fixes to them is generated. Clustering reduces the burden on the instructor and also the variations that have to be handled during feedback generation. The verified feedback generation ensures that only correct feedback is generated. We have applied this methodology to iterative dynamic programming (DP) assignments. Our clustering technique uses features of DP solutions. We have designed a novel counter-example guided feedback generation algorithm capable of suggesting fixes to all faults in a submission. In an evaluation on 2226 submissions to 4 problems, we could generate verified feedback for 1911 (85%) submissions in 1.6s each on an average. Our technique does a good job of reducing the burden on the instructor. Only one submission had to be manually validated or added for every 16 submissions.

cs.SE↗

arXiv2015

Automatic Synthesis of Geometry Problems for an Intelligent Tutoring System

This paper presents an intelligent tutoring system, GeoTutor, for Euclidean Geometry that is automatically able to synthesize proof problems and their respective solutions given a geometric figure together with a set of properties true of it. GeoTutor can provide personalized practice problems that address student deficiencies in the subject matter.

cs.AI↗

arXiv2015

Program Synthesis using Natural Language

Interacting with computers is a ubiquitous activity for millions of people. Repetitive or specialized tasks often require creation of small, often one-off, programs. End-users struggle with learning and using the myriad of domain-specific languages (DSLs) to effectively accomplish these tasks. We present a general framework for constructing program synthesizers that take natural language (NL) inputs and produce expressions in a target DSL. The framework takes as input a DSL definition and training data consisting of NL/DSL pairs. From these it constructs a synthesizer by learning optimal weights and classifiers (using NLP features) that rank the outputs of a keyword-programming based translation. We applied our framework to three domains: repetitive text editing, an intelligent tutoring system, and flight information queries. On 1200+ English descriptions, the respective synthesizers rank the desired program as the top-1 and top-3 for 80% and 90% descriptions respectively.

cs.PL↗

arXiv2014

Automatic Generation of Alternative Starting Positions for Simple Traditional Board Games

Simple board games, like Tic-Tac-Toe and CONNECT-4, play an important role not only in the development of mathematical and logical skills, but also in the emotional and social development. In this paper, we address the problem of generating targeted starting positions for such games. This can facilitate new approaches for bringing novice players to mastery, and also leads to discovery of interesting game variants. We present an approach that generates starting states of varying hardness levels for player~$1$ in a two-player board game, given rules of the board game, the desired number of steps required for player~$1$ to win, and the expertise levels of the two players. Our approach leverages symbolic methods and iterative simulation to efficiently search the extremely large state space. We present experimental results that include discovery of states of varying hardness levels for several simple grid-based board games. The presence of such states for standard game variants like $4 \times 4$ Tic-Tac-Toe opens up new games to be played that have never been played as the default start state is heavily biased.

cs.AI↗

arXiv2014

Feedback Generation for Performance Problems in Introductory Programming Assignments

Providing feedback on programming assignments manually is a tedious, error prone, and time-consuming task. In this paper, we motivate and address the problem of generating feedback on performance aspects in introductory programming assignments. We studied a large number of functionally correct student solutions to introductory programming assignments and observed: (1) There are different algorithmic strategies, with varying levels of efficiency, for solving a given problem. These different strategies merit different feedback. (2) The same algorithmic strategy can be implemented in countless different ways, which are not relevant for reporting feedback on the student program. We propose a light-weight programming language extension that allows a teacher to define an algorithmic strategy by specifying certain key values that should occur during the execution of an implementation. We describe a dynamic analysis based approach to test whether a student's program matches a teacher's specification. Our experimental results illustrate the effectiveness of both our specification language and our dynamic analysis. On one of our benchmarks consisting of 2316 functionally correct implementations to 3 programming problems, we identified 16 strategies that we were able to describe using our specification language (in 95 minutes after inspecting 66, i.e., around 3%, implementations). Our dynamic analysis correctly matched each implementation with its corresponding specification, thereby automatically producing the intended feedback.

cs.PL↗

arXiv2012

Automated Feedback Generation for Introductory Programming Assignments

We present a new method for automatically providing feedback for introductory programming problems. In order to use this method, we need a reference implementation of the assignment, and an error model consisting of potential corrections to errors that students might make. Using this information, the system automatically derives minimal corrections to student's incorrect solutions, providing them with a quantifiable measure of exactly how incorrect a given solution was, as well as feedback about what they did wrong. We introduce a simple language for describing error models in terms of correction rules, and formally define a rule-directed translation strategy that reduces the problem of finding minimal corrections in an incorrect program to the problem of synthesizing a correct program from a sketch. We have evaluated our system on thousands of real student attempts obtained from 6.00 and 6.00x. Our results show that relatively simple error models can correct on average 65% of all incorrect submissions.

cs.PL↗

arXiv2012

Textual Features for Programming by Example

In Programming by Example, a system attempts to infer a program from input and output examples, generally by searching for a composition of certain base functions. Performing a naive brute force search is infeasible for even mildly involved tasks. We note that the examples themselves often present clues as to which functions to compose, and how to rank the resulting programs. In text processing, which is our domain of interest, clues arise from simple textual features: for example, if parts of the input and output strings are permutations of one another, this suggests that sorting may be useful. We describe a system that learns the reliability of such clues, allowing for faster search and a principled ranking over programs. Experiments on a prototype of this system show that this learning scheme facilitates efficient inference on a range of text processing tasks.

cs.AI↗

arXiv2012

Learning Semantic String Transformations from Examples

We address the problem of performing semantic transformations on strings, which may represent a variety of data types (or their combination) such as a column in a relational table, time, date, currency, etc. Unlike syntactic transformations, which are based on regular expressions and which interpret a string as a sequence of characters, semantic transformations additionally require exploiting the semantics of the data type represented by the string, which may be encoded as a database of relational tables. Manually performing such transformations on a large collection of strings is error prone and cumbersome, while programmatic solutions are beyond the skill-set of end-users. We present a programming by example technology that allows end-users to automate such repetitive tasks. We describe an expressive transformation language for semantic manipulation that combines table lookup operations and syntactic manipulations. We then present a synthesis algorithm that can learn all transformations in the language that are consistent with the user-provided set of input-output examples. We have implemented this technology as an add-in for the Microsoft Excel Spreadsheet system and have evaluated it successfully over several benchmarks picked from various Excel help-forums.

cs.DB↗

arXiv2012

Bound Analysis of Imperative Programs with the Size-change Abstraction (extended version)

The size-change abstraction (SCA) is an important program abstraction for termination analysis, which has been successfully implemented in many tools for functional and logic programs. In this paper, we demonstrate that SCA is also a highly effective abstract domain for the bound analysis of imperative programs. We have implemented a bound analysis tool based on SCA for imperative programs. We abstract programs in a pathwise and context dependent manner, which enables our tool to analyze real-world programs effectively. Our work shows that SCA captures many of the essential ideas of previous termination and bound analysis and goes beyond in a conceptually simpler framework.

cs.PL↗