Shaiful Chowdhury

Hisham Kidwai, Danika Passler Bates, Sujana Islam Suhi et al.

Robots can greatly enhance human capabilities, yet their development presents a range of challenges. This collaborative study, conducted by a team of software engineering and robotics researchers, seeks to identify the challenges encountered by robot developers by analyzing questions posted on StackOverflow. We created a filtered dataset of 500 robotics-related questions and examined their characteristics, comparing them with randomly selected questions from the platform. Our findings indicate that the small size of the robotics community limits the visibility of these questions, resulting in fewer responses. While the number of robotics questions has been steadily increasing, they remain less popular than the average question and answer on StackOverflow. This underscores the importance of research that focuses on the challenges faced by robotics practitioners. Consequently, we conducted a thematic analysis of the 500 robotics questions to uncover common inquiry patterns. We identified 11 major themes, with questions about robot movement being the most frequent. Our analysis of yearly trends revealed that certain themes, such as Specifications, were prominent from 2009 to 2014 but have since diminished in relevance. In contrast, themes like Moving, Actuator, and Remote have consistently dominated discussions over the years. These findings suggest that challenges in robotics may vary over time. Notably, the majority of robotics questions are framed as How questions, rather than Why or What questions, revealing the lack of enough resources for the practitioners. These insights can help guide researchers and educators in developing effective and timely educational materials for robotics practitioners.

Alexander William Wong, Amir Salimi, Shaiful Chowdhury et al.

One problem when studying how to find and fix syntax errors is how to get natural and representative examples of syntax errors. Most syntax error datasets are not free, open, and public, or they are extracted from novice programmers and do not represent syntax errors that the general population of developers would make. Programmers of all skill levels post questions and answers to Stack Overflow which may contain snippets of source code along with corresponding text and tags. Many snippets do not parse, thus they are ripe for forming a corpus of syntax errors and corrections. Our primary contribution is an approach for extracting natural syntax errors and their corresponding human made fixes to help syntax error research. A Python abstract syntax tree parser is used to determine preliminary errors and corrections on code blocks extracted from the SOTorrent data set. We further analyzed our code by executing the corrections in a Python interpreter. We applied our methodology to produce a public data set of 62,965 Python Stack Overflow code snippets with corresponding tags, errors, and stack traces. We found that errors made by Stack Overflow users do not match errors made by student developers or random mutations, implying there is a serious representativeness risk within the field. Finally we share our dataset openly so that future researchers can re-use and extend our syntax errors and fixes.