Hridesh Rajan's research while affiliated with Iowa State University and other places
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Publications (203)
Recent work has shown that Machine Learning (ML) programs are error-prone and called for contracts for ML code. Contracts, as in the design by contract methodology, help document APIs and aid API users in writing correct code. The question is: what kinds of contracts would provide the most help to API users? We are especially interested in what kin...
Deep neural networks (DNNs) are susceptible to bugs, just like other types of software systems. A significant uptick in using DNN, and its applications in wide-ranging areas, including safety-critical systems, warrant extensive research on software engineering tools for improving the reliability of DNN-based systems. One such tool that has gained s...
Recent work has shown that Machine Learning (ML) programs are error-prone and called for contracts for ML code. Contracts, as in the design by contract methodology, help document APIs and aid API users in writing correct code. The question is: what kinds of contracts would provide the most help to API users? We are especially interested in what kin...
Deep Learning (DL) applications are being used to solve problems in critical domains (e.g., autonomous driving or medical diagnosis systems). Thus, developers need to debug their systems to ensure that the expected behavior is delivered. However, it is hard and expensive to debug DNNs. When the failure symptoms or unsatisfied accuracies are reporte...
Machine learning (ML) is increasingly being used in critical decision-making software, but incidents have raised questions about the fairness of ML predictions. To address this issue, new tools and methods are needed to mitigate bias in ML-based software. Previous studies have proposed bias mitigation algorithms that only work in specific situation...
R and Python are among the most popular languages used in many critical data analytics tasks. However, we still do not fully understand the capabilities of these two languages w.r.t. bugs encountered in data analytics tasks. What type of bugs are common? What are the main root causes? What is the relation between bugs and root causes? How to mitiga...
Can we take a recurrent neural network (RNN) trained to translate between languages and augment it to support a new natural language without retraining the model from scratch? Can we fix the faulty behavior of the RNN by replacing portions associated with the faulty behavior? Recent works on decomposing a fully connected neural network (FCNN) and c...
Machine Learning (ML) software has been widely adopted in modern society, with reported fairness implications for minority groups based on race, sex, age, etc. Many recent works have proposed methods to measure and mitigate algorithmic bias in ML models. The existing approaches focus on single classifier-based ML models. However, real-world ML mode...
Fairness of machine learning (ML) software has become a major concern in the recent past. Although recent research on testing and improving fairness have demonstrated impact on real-world software, providing fairness guarantee in practice is still lacking. Certification of ML models is challenging because of the complex decision-making process of t...
![Figure 1: An example of NLP reuse-related bug [34]](publication/365942765/figure/fig1/AS:11431281104191273@1669974490432/An-example-of-NLP-reuse-related-bug-34_Q320.jpg)
In NLP, reusing pre-trained models instead of training from scratch has gained popularity; however, NLP models are mostly black boxes, very large, and often require significant resources. To ease, models trained with large corpora are made available, and developers reuse them for different problems. In contrast, developers mostly build their models...
Powered by advances in deep learning (DL) techniques, machine learning and artificial intelligence have achieved astonishing successes. However, the rapidly growing needs for DL also led to communication- and resource-intensive distributed training jobs for large-scale DL training, which are typically deployed over GPU clusters. To sustain the ever...
We propose PASS , a O ( n ) algorithm for data reduction that is specifically aimed at preserving the semantics of time series data visualization in the form of line chart. Visualization of large trend line data is a challenge and current sampling approaches do produce reduction but result in loss of semantics and anomalous behavior. We have evalua...
Fueled by advances in distributed deep learning (DDL), recent years have witnessed a rapidly growing demand for resource-intensive distributed/parallel computing to process DDL computing jobs. To resolve network communication bottleneck and load balancing issues in distributed computing, the so-called ``ring-all-reduce'' decentralized architecture...
Deep Neural Networks (DNNs) are used in a wide variety of applications. However, as in any software application, DNN-based apps are afflicted with bugs. Previous work observed that DNN bug fix patterns are different from traditional bug fix patterns. Furthermore, those buggy models are non-trivial to diagnose and fix due to inexplicit errors with s...
Today deep learning is widely used for building software. A software engineering problem with deep learning is that finding an appropriate convolutional neural network (CNN) model for the task can be a challenge for developers. Recent work on AutoML, more precisely neural architecture search (NAS), embodied by tools like Auto-Keras aims to solve th...
Increasingly larger number of software systems today are including data science components for descriptive, predictive, and prescriptive analytics. The collection of data science stages from acquisition, to cleaning/curation, to modeling, and so on are referred to as data science pipelines. To facilitate research and practice on data science pipeli...
Training from scratch is the most common way to build a Convolutional Neural Network (CNN) based model. What if we can build new CNN models by reusing parts from previously build CNN models? What if we can improve a CNN model by replacing (possibly faulty) parts with other parts? In both cases, instead of training, can we identify the part responsi...
Implicit deep learning has received increasing attention recently due to the fact that it generalizes the recursive prediction rules of many commonly used neural network architectures. Its prediction rule is provided implicitly based on the solution of an equilibrium equation. Although a line of recent empirical studies has demonstrated its superio...
In recent years, many incidents have been reported where machine learning models exhibited discrimination among people based on race, sex, age, etc. Research has been conducted to measure and mitigate unfairness in machine learning models. For a machine learning task, it is a common practice to build a pipeline that includes an ordered set of data...
Deep neural networks (DNNs) are becoming an integral part of most software systems. Previous work has shown that DNNs have bugs. Unfortunately, existing debugging techniques do not support localizing DNN bugs because of the lack of understanding of model behaviors. The entire DNN model appears as a black box. To address these problems, we propose a...
Background: Scientists around the world use NCBI’s non-redundant (NR) database to identify the taxonomic origin and functional annotation of their favorite protein sequences using BLAST. Unfortunately, due to the exponential growth of this database, many scientists do not have a good understanding of the contents of the NR database. There is a need...
Motivation:
As the cost of sequencing decreases, the amount of data being deposited into public repositories is increasing rapidly. Public databases rely on the user to provide metadata for each submission that is prone to user error. Unfortunately, most public databases, such as non-redundant (NR), rely on user input and do not have methods for i...
Machine learning models are increasingly being used in important decision-making software such as approving bank loans, recommending criminal sentencing, hiring employees, and so on. It is important to ensure the fairness of these models so that no discrimination is made between different groups in a protected attribute (e.g., race, sex, age) while...
Many data-driven software engineering tasks such as discovering programming patterns, mining API specifications, etc., perform source code analysis over control flow graphs (CFGs) at scale. Analyzing millions of CFGs can be expensive and performance of the analysis heavily depends on the underlying CFG traversal strategy. State-of-the-art analysis...
Significant interest in applying Deep Neural Network (DNN) has fueled the need to support engineering of software that uses DNNs. Repairing software that uses DNNs is one such unmistakable SE need where automated tools could be beneficial; however, we do not fully understand challenges to repairing and patterns that are utilized when manually repai...
Background:
Creating a scalable computational infrastructure to analyze the wealth of information contained in data repositories is difficult due to significant barriers in organizing, extracting and analyzing relevant data. Shared data science infrastructures like Boag is needed to efficiently process and parse data contained in large data reposi...
Deep learning has gained substantial popularity in recent years. Developers mainly rely on libraries and tools to add deep learning capabilities to their software. What kinds of bugs are frequently found in such software? What are the root causes of such bugs? What impacts do such bugs have? Which stages of deep learning pipeline are more bug prone...
Modern software systems are increasingly including machine learning (ML) as an integral component. However, we do not yet understand the difficulties faced by software developers when learning about ML libraries and using them within their systems. To that end, this work reports on a detailed (manual) examination of 3,243 highly-rated Q&A posts rel...
Background: Creating a scalable computational infrastructure to analyze the wealth of information contained in data repositories is difficult due to significant barriers in organizing, extracting and analyzing relevant data. Shared data science infrastructures like Boa_g is needed to efficiently process and parse data contained in large data reposi...
Despite numerous attempts to defend deep learning based image classifiers, they remain susceptible to the adversarial attacks. This paper proposes a technique to identify susceptible classes, those classes that are more easily subverted. To identify the susceptible classes we use distance-based measures and apply them on a trained model. Based on t...
Deep learning has gained substantial popularity in recent years. Developers mainly rely on libraries and tools to add deep learning capabilities to their software. What kinds of bugs are frequently found in such software? What are the root causes of such bugs? What impacts do such bugs have? Which stages of deep learning pipeline are more bug prone...
Big data-driven transportation engineering has the potential to improve utilization of road infrastructure, decrease traffic fatalities, improve fuel consumption, and decrease construction worker injuries, among others. Despite these benefits, research on big data-driven transportation engineering is difficult today due to the computational experti...
Background: Creating a scalable computational infrastructure to analyze the wealth of information contained in data repositories is difficult due to significant barriers in organizing, extracting and analyzing relevant data. Shared data science infrastructures like Boa_g is needed to efficiently process and parse data contained in large data reposi...
Despite numerous attempts to defend deep learning based image classifiers, they remain susceptible to the adversarial attacks. This paper proposes a technique to identify susceptible classes, those classes that are more easily subverted. To identify the susceptible classes we use distance-based measures and apply them on a trained model. Based on t...
Modern software relies on libraries and uses them via application programming interfaces (APIs). Correct API usage as well as many software engineering tasks are enabled when APIs have formal specifications. In this work, we analyze the implementation of each method in an API to infer a formal postcondition. Conventional wisdom is that, if one has...
Background: Creating a scalable computational infrastructure to analyze the wealth of information contained in data repositories is difficult due to significant barriers in organizing, extracting and analyzing relevant data. Shared data science infrastructures like Boa_g is needed to efficiently process and parse data contained in large data reposi...
The preconditions of an API method are constraints on the states of its receiver object and arguments intended by the library designer(s) to correctly invoke it in the client code. There have been two main kinds of approaches for automatically inferring API preconditions. The first kind of approaches mines the frequently checked conditions guarding...
The programmers utilize APIs provided by frameworks and libraries to avoid reinventing the wheel and API specifications aid them to fully understand and adequately use these APIs. This paper introduces "contract-based typestate specifications", a new kind ofspecification for documenting programs. Prior work has focused on two kinds of specification...
A majority of modern software is constructed using languages that compute by producing side-effects such as reading/writing from/to files, throwing exceptions, acquiring locks, etc. To understand a piece of software, e.g. a class, it is important for a developer to understand its side-effects. Similarly, to replace a class with another, it is impor...
Manually writing pre- and postconditions to document the behavior of a large library is a time-consuming task; what is needed is a way to automatically infer them. Conventional wisdom is that, if one has preconditions, then one can use the strongest postcondition predicate transformer (SP) to infer postconditions. However, we have performed a study...
Programmers often consult an online Q&A forum such as Stack Overflow to learn new APIs. This paper presents an empirical study on the prevalence and severity of API misuse on Stack Overflow. To reduce manual assessment effort, we design ExampleCheck, an API usage mining framework that extracts patterns from over 380K Java repositories on GitHub and...
Popularity of data-driven software engineering has led to an increasing demand on the infrastructures to support efficient execution of tasks that require deeper source code analysis. While task optimization and parallelization are the adopted solutions, other research directions are less explored. We present collective program analysis (CPA), a te...
Source code analysis at a large scale is useful for solving many software engineering problems, however, could be very expensive, thus, making its use difficult. This work proposes hybrid traversal, a technique for performing source code analysis over control flow graphs more efficiently. Analysis over a control flow graph requires traversing the g...
Big Data-driven transportation engineering has the potential to improve utilization of road infrastructure, decrease traffic fatalities, improve fuel consumption, decrease construction worker injuries, among others. Despite these benefits, research on Big Data-driven transportation engineering is difficult today due to the computational expertise r...
Background
Creating a computational infrastructure to analyze the wealth of information contained in data repositories that scales well is difficult due to significant barriers in organizing, extracting and analyzing relevant data. Shared Data Science Infrastructures like Boa can be used to more efficiently process and parse data contained in large...
Encouraged by the success of data-driven software engineering (SE) techniques that have found numerous applications e.g. in defect prediction, specification inference, the demand for mining and analyzing source code repositories at scale has significantly increased. However, analyzing source code at scale remains expensive to the extent that data-d...
Asynchronous message passing concurrency with higher level concurrency constructs including activities, asynchronous method invocations and future return values is gaining increased popularity, as an alternative to shared memory concurrency with lower level threads and locks. However, similar to data races in shared memory concurrency, message race...
Frameworks and libraries provide application programming interfaces (APIs) that serve as building blocks in modern software development. As APIs present the opportunity of increased productivity, it also calls for correct use to avoid buggy code. The usage-based specification mining technique has shown great promise in solving this problem through...
This paper introduces a novel type-and-effect calculus, first-class effects, where the computational effect of an expression can be programmatically reflected, passed around as values, and analyzed at run time. A broad range of designs "hard-coded" in existing effect-guided analyses — from thread scheduling, version-consistent software updating, to...
This paper introduces a novel type-and-effect calculus, first-class effects, where the computational effect of an expression can be programmatically reflected, passed around as values, and analyzed at run time. A broad range of designs "hard-coded" in existing effect-guided analyses — from thread scheduling, version-consistent software updating, to...
Advances in programming often revolve around key design patterns, which programming languages embody as new control features. These control features, such as higher-order functions, advice, and context dependence, use indirection to decrease coupling and enhance modularity. However, this indirection makes them difficult to verify, because it hides...
Separating crosscutting concerns while preserving modular reasoning is challenging. Type-based interfaces (event types) separate modularized crosscutting concerns (observers) and traditional object-oriented concerns (subjects). Event types paired with event specifications were shown to be effective in enabling modular reasoning about subjects and o...
We introduce Candoia, a platform and ecosystem for building Mining Software Repositories (MSR) tools. The platform is designed to support building of MSR tools by providing necessary tools and abstractions that hide the complex details of version control, bug databases, source code programming languages and forges. The ecosystem allows easy sharing...
The need for concurrency in modern software is increasingly fulfilled by utilizing the message passing paradigm because of its modularity and scalability. In the message passing paradigm, concurrently running processes communicate by sending and receiving messages. Asynchronous messaging introduces the possibility of message ordering problems: two...
Implicit concurrency between handlers is important for event driven systems because it helps simultaneously promote modularity and scalability. Knowing the side-effect of the handlers is critical in these systems to avoid concurrency hazards such as data races. As event systems are dynamic because statically known and unknown handlers can register...
In today's software-centric world, ultra-large-scale software repositories, such as SourceForge, GitHub, and Google Code, are the new library of Alexandria. They contain an enormous corpus of software and related information. Scientists and engineers alike are interested in analyzing this wealth of information. However, systematic extraction and an...
Formal specifications for APIs help developers correctly use them and enable checker tools automatically verify their uses. However, formal specifications are not always available with released APIs. In this work, we demonstrate an approach for mining API preconditions from a large-scale corpus of open-source software. It considers conditions guard...
Programming language researchers often study real-world projects to see how language features have been adopted and are being used. Typically researchers choose a small number of projects to study, due to the immense challenges associated with finding, downloading, storing, processing, and querying large amounts of data. The Boa programming languag...
Efficient mapping of message passing concurrency (MPC) abstractions to Java Virtual Machine (JVM) threads is critical for performance, scalability, and CPU utilization; but tedious and time consuming to perform manually. In general, this mapping cannot be found in polynomial time, but we show that by exploiting the local characteristics of MPC abst...
Separating crosscutting concerns while preserving modular reasoning is challenging. Type-based interfaces (event types) separate modularized crosscutting concerns (observers) and traditional object-oriented concerns (subjects). Event types paired with event specifications were shown to be effective in enabling modular reasoning about subjects and o...