arXiv subjects

Gias Uddin

Publications and source records attributed to Gias Uddin.

65 records · Page 4Linked to original sources

arXiv2021

Quality Assurance Challenges for Machine Learning Software Applications During Software Development Life Cycle Phases

In the past decades, the revolutionary advances of Machine Learning (ML) have shown a rapid adoption of ML models into software systems of diverse types. Such Machine Learning Software Applications (MLSAs) are gaining importance in our daily lives. As such, the Quality Assurance (QA) of MLSAs is of paramount importance. Several research efforts are dedicated to determining the specific challenges we can face while adopting ML models into software systems. However, we are aware of no research that offered a holistic view of the distribution of those ML quality assurance challenges across the various phases of software development life cycles (SDLC). This paper conducts an in-depth literature review of a large volume of research papers that focused on the quality assurance of ML models. We developed a taxonomy of MLSA quality assurance issues by mapping the various ML adoption challenges across different phases of SDLC. We provide recommendations and research opportunities to improve SDLC practices based on the taxonomy. This mapping can help prioritize quality assurance efforts of MLSAs where the adoption of ML models can be considered crucial.

cs.SE↗

arXiv2021

How do developers discuss and support new programming languages in technical Q&A site? An empirical study of Go, Swift, and Rust in Stack Overflow

New programming languages (e.g., Swift, Go, Rust, etc.) are being introduced to provide a better opportunity for the developers to make software development robust and easy. At the early stage, a programming language is likely to have resource constraints that encourage the developers to seek help frequently from experienced peers active in QA sites such as Stack Overflow (SO). In this study, we have formally studied the discussions on three popular new languages introduced after the inception of SO (2008) and match those with the relevant activities in GitHub whenever appropriate. For that purpose, we have mined 4,17,82,536 questions and answers from SO and 7,846 issue information along with 6,60,965 repository information from GitHub. Initially, the development of new languages is relatively slow compared to mature languages (e.g., C, C++, Java). The expected outcome of this study is to reveal the difficulties and challenges faced by the developers working with these languages so that appropriate measures can be taken to expedite the generation of relevant resources. We have used the LDA method on SO's questions and answers to identify different topics of new languages. We have extracted several features of the answer pattern of the new languages from SO to study their characteristics. These attributes were used to identify difficult topics. We explored the background of developers who are contributing to these languages. We have created a model by combining Stack Overflow data and issues, repository, user data of GitHub. Finally, we have used that model to identify factors that affect language evolution. We believe that the outcome of our study is likely to help the owner/sponsor of these languages to design better features and documentation. It will also help the software developers or students to prepare themselves to work on these languages in an informed way.

cs.SE↗

arXiv2021

Security and Machine Learning Adoption in IoT: A Preliminary Study of IoT Developer Discussions

Internet of Things (IoT) is defined as the connection between places and physical objects (i.e., things) over the internet/network via smart computing devices. Traditionally, we learn about the IoT ecosystem/problems by conducting surveys of IoT developers/practitioners. Another way to learn is by analyzing IoT developer discussions in popular online developer forums like Stack Overflow (SO). However, we are aware of no such studies that focused on IoT developers' security and ML-related discussions in SO. This paper offers the results of preliminary study of IoT developer discussions in SO. We find around 12% of sentences contain security discussions, while around 0.12% sentences contain ML- related discussions. We find that IoT developers discussing security issues frequently inquired about how the shared data can be stored, shared, and transferred securely across IoT devices and users. We also find that IoT developers are interested to adopt deep neural network-based ML models into their IoT devices, but they find it challenging to accommodate those into their resource-constrained IoT devices. Our findings offer implications for IoT vendors and researchers to develop and design novel techniques for improved security and ML adoption into IoT devices.

cs.CR↗

arXiv2021

A Benchmark Study of Machine Learning Models for Online Fake News Detection

The proliferation of fake news and its propagation on social media has become a major concern due to its ability to create devastating impacts. Different machine learning approaches have been suggested to detect fake news. However, most of those focused on a specific type of news (such as political) which leads us to the question of dataset-bias of the models used. In this research, we conducted a benchmark study to assess the performance of different applicable machine learning approaches on three different datasets where we accumulated the largest and most diversified one. We explored a number of advanced pre-trained language models for fake news detection along with the traditional and deep learning ones and compared their performances from different aspects for the first time to the best of our knowledge. We find that BERT and similar pre-trained models perform the best for fake news detection, especially with very small dataset. Hence, these models are significantly better option for languages with limited electronic contents, i.e., training data. We also carried out several analysis based on the models' performance, article's topic, article's length, and discussed different lessons learned from them. We believe that this benchmark study will help the research community to explore further and news sites/blogs to select the most appropriate fake news detection method.

cs.CL↗

arXiv2021

An Empirical Study of Developer Discussions on Low-Code Software Development Challenges

Low-code software development (LCSD) is an emerging paradigm that combines minimal source code with interactive graphical interfaces to promote rapid application development. LCSD aims to democratize application development to software practitioners with diverse backgrounds. Given that LCSD is relatively a new paradigm, it is vital to learn about the challenges developers face during their adoption of LCSD platforms. The online developer forum, Stack Overflow (SO), is popular among software developers to ask for solutions to their technical problems. We observe a growing body of posts in SO with discussions of LCSD platforms. In this paper, we present an empirical study of around 5K SO posts (questions + accepted answers) that contain discussions of nine popular LCSD platforms. We apply topic modeling on the posts to determine the types of topics discussed. We find 13 topics related to LCSD in SO. The 13 topics are grouped into four categories: Customization, Platform Adoption, Database Management, and Third-Party Integration. More than 40% of the questions are about customization, i.e., developers frequently face challenges with customizing user interfaces or services offered by LCSD platforms. The topic "Dynamic Event Handling" under the "Customization" category is the most popular (in terms of average view counts per question of the topic) as well as the most difficult. It means that developers frequently search for customization solutions such as how to attach dynamic events to a form in low-code UI, yet most (75.9%) of their questions remain without an accepted answer. We manually label 900 questions from the posts to determine the prevalence of the topics' challenges across LCSD phases. We find that most of the questions are related to the development phase, and low-code developers also face challenges with automated testing.

cs.SE↗

arXiv2021

Mining API Usage Scenarios from Stack Overflow

We propose a framework to mine API usage scenarios from Stack Overflow. Each task consists of a code example, the task description, and the reactions of developers towards the code example. First, we present an algorithm to automatically link a code example in a forum post to an API mentioned in the textual contents of the forum post. Second, we generate a natural language description of the task by summarizing the discussions around the code example. Third, we automatically associate developers reactions (i.e., positive and negative opinions) towards the code example to offer information about code quality. We evaluate the algorithms using three benchmarks.

cs.SE↗

arXiv2021

Automatic Detection of Five API Documentation Smells: Practitioners' Perspectives

The learning and usage of an API is supported by official documentation. Like source code, API documentation is itself a software product. Several research results show that bad design in API documentation can make the reuse of API features difficult. Indeed, similar to code smells or code antipatterns, poorly designed API documentation can also exhibit 'smells'. Such documentation smells can be described as bad documentation styles that do not necessarily produce an incorrect documentation but nevertheless make the documentation difficult to properly understand and to use. Recent research on API documentation has focused on finding content inaccuracies in API documentation and to complement API documentation with external resources (e.g., crowd-shared code examples). We are aware of no research that focused on the automatic detection of API documentation smells. This paper makes two contributions. First, we produce a catalog of five API documentation smells by consulting literature on API documentation presentation problems. We create a benchmark dataset of 1,000 API documentation units by exhaustively and manually validating the presence of the five smells in Java official API reference and instruction documentation. Second, we conduct a survey of 21 professional software developers to validate the catalog. The developers agreed that they frequently encounter all five smells in API official documentation and 95.2% of them reported that the presence of the documentation smells negatively affects their productivity. The participants wished for tool support to automatically detect and fix the smells in API official documentation. We develop a suite of rule-based, deep and shallow machine learning classifiers to automatically detect the smells. The best performing classifier BERT, a deep learning model, achieves F1-scores of 0.75 - 0.97.

cs.SE↗

arXiv2021

Understanding How and Why Developers Seek and Analyze API-related Opinions

With the advent and proliferation of online developer forums as informal documentation, developers often share their opinions about the APIs they use. Thus, opinions of others often shape the developer's perception and decisions related to software development. For example, the choice of an API or how to reuse the functionality the API offers are, to a considerable degree, conditioned upon what other developers think about the API. While many developers refer to and rely on such opinion-rich information about APIs, we found little research that investigates the use and benefits of public opinions. To understand how developers seek and evaluate API opinions, we conducted two surveys involving a total of 178 software developers. We analyzed the data in two dimensions, each corresponding to specific needs related to API reviews: (1) Needs for seeking API reviews, and (2) Needs for automated tool support to assess the reviews. We observed that developers seek API reviews and often have to summarize those for diverse development needs (e.g., API suitability). Developers also make conscious efforts to judge the trustworthiness of the provided opinions and believe that automated tool support for API reviews analysis can assist in diverse development scenarios, including, for example, saving time in API selection as well as making informed decisions on a particular API features.

cs.SE↗

arXiv2021

Automatic API Usage Scenario Documentation from Technical Q&A Sites

The online technical Q&A site Stack Overflow (SO) is popular among developers to support their coding and diverse development needs. To address shortcomings in API official documentation resources, several research has thus focused on augmenting official API documentation with insights (e.g., code examples) from SO. The techniques propose to add code examples/insights about APIs into its official documentation. Reviews are opinionated sentences with positive/negative sentiments. However, we are aware of no previous research that attempts to automatically produce API documentation from SO by considering both API code examples and reviews. In this paper, we present two novel algorithms that can be used to automatically produce API documentation from SO by combining code examples and reviews towards those examples. The first algorithm is called statistical documentation, which shows the distribution of positivity and negativity around the code examples of an API using different metrics (e.g., star ratings). The second algorithm is called concept-based documentation, which clusters similar and conceptually relevant usage scenarios. An API usage scenario contains a code example, a textual description of the underlying task addressed by the code example, and the reviews (i.e., opinions with positive and negative sentiments) from other developers towards the code example. We deployed the algorithms in Opiner, a web-based platform to aggregate information about APIs from online forums. We evaluated the algorithms by mining all Java JSON-based posts in SO and by conducting three user studies based on produced documentation from the posts.

cs.SE↗

arXiv2021

An Empirical Study of C++ Vulnerabilities in Crowd-Sourced Code Examples

Software developers share programming solutions in Q&A sites like Stack Overflow. The reuse of crowd-sourced code snippets can facilitate rapid prototyping. However, recent research shows that the shared code snippets may be of low quality and can even contain vulnerabilities. This paper aims to understand the nature and the prevalence of security vulnerabilities in crowd-sourced code examples. To achieve this goal, we investigate security vulnerabilities in the C++ code snippets shared on Stack Overflow over a period of 10 years. In collaborative sessions involving multiple human coders, we manually assessed each code snippet for security vulnerabilities following CWE (Common Weakness Enumeration) guidelines. From the 72,483 reviewed code snippets used in at least one project hosted on GitHub, we found a total of 69 vulnerable code snippets categorized into 29 types. Many of the investigated code snippets are still not corrected on Stack Overflow. The 69 vulnerable code snippets found in Stack Overflow were reused in a total of 2859 GitHub projects. To help improve the quality of code snippets shared on Stack Overflow, we developed a browser extension that allow Stack Overflow users to check for vulnerabilities in code snippets when they upload them on the platform.

cs.SE↗

arXiv2017

Resolving API Mentions in Informal Documents

Developer forums contain opinions and information related to the usage of APIs. API names in forum posts are often not explicitly linked to their official resources. Automatic linking of an API mention to its official resources can be challenging for various reasons, such as, name overloading. We present a technique, ANACE, to automatically resolve API mentions in the textual contents of forum posts. Given a database of APIs, we first detect all words in a forum post that are potential references to an API. We then use a combination of heuristics and machine learning to eliminate false positives and to link true positives to the actual APIs and their resources.

cs.SE↗