Meher Bhardwaj

Amit Kumar, Mahen Gandhi, Meher Bhardwaj et al.

Open-source projects often rely on a small group of highly active contributors known as hero developers. Prior work shows that hero developers are common in many OSS and enterprise projects, yet who qualifies as a hero depends heavily on the chosen contribution metric. Code-based metrics identify implementation-focused developers, whereas discussion-based metrics highlight coordination and communication; these metrics capture distinct facets of contribution. We conducted a measurement-sensitive study of multifaceted heroism across 77 Apache Software Foundation projects using three technical measures (commit count, distinct files touched, churn) and two social measures (issue-comment count, number of distinct issues commented on). We examined hero prevalence, overlap among hero sets, and severity-wise bug-fixing outcomes via fix and reopen rates. Results show that hero projects are common under all measures, but identified heroes differ substantially across facets. The pooled Jaccard overlap between technical and social hero sets is only 0.10. Cross-facet asymmetry is evident: 71.4% of technical heroes exhibit strong social activity, while only 24.2% of social heroes show strong technical activity. Fix-rate and reopen-rate differences are modest, yet hero-category rankings vary across severity levels and outcome measures. These findings indicate that heroism is not a single, metric-independent role. A multifaceted perspective offers a more reliable understanding of key contributors and better supports developer prioritisation and severity-aware bug assignment.

Meher Bhardwaj, Hrishikesh Ethari, Dennis Singh Moirangthem

The SQL-to-text generation task traditionally uses template base, Seq2Seq, tree-to-sequence, and graph-to-sequence models. Recent models take advantage of pre-trained generative language models for this task in the Seq2Seq framework. However, treating SQL as a sequence of inputs to the pre-trained models is not optimal. In this work, we put forward a new SQL intermediate representation called EzSQL to align SQL with the natural language text sequence. EzSQL simplifies the SQL queries and brings them closer to natural language text by modifying operators and keywords, which can usually be described in natural language. EzSQL also removes the need for set operators. Our proposed SQL-to-text generation model uses EzSQL as the input to a pre-trained generative language model for generating the text descriptions. We demonstrate that our model is an effective state-of-the-art method to generate text narrations from SQL queries on the WikiSQL and Spider datasets. We also show that by generating pretraining data using our SQL-to-text generation model, we can enhance the performance of Text-to-SQL parsers.