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SQL is the most commonly used front-end language for data-intensive scalable computing (DISC) applications due to its broad presence in new and legacy workflows and shallow learning curve. However, DISC-backed SQL introduces several layers of abstraction that significantly reduce the visibility and transparency of workflows, making it challenging for developers to find and fix errors in a query. When a query returns incorrect outputs, it takes a non-trivial effort to comprehend every stage of the query execution and find the root cause among the input data and complex SQL query. We aim to bring the benefits of step-through interactive debugging to DISC-powered SQL with DeSQL. Due to the declarative nature of SQL, there are no ordered atomic statements to place a breakpoint to monitor the flow of data. DeSQL’s automated query decomposition breaks a SQL query into its constituent sub queries, offering natural locations for setting breakpoints and monitoring intermediate data. However, due to advanced query optimization and translation in DISC systems, a user query rarely matches the physical execution, making it challenging to associate subqueries with their intermediate data. DeSQL performs fine-grained taint analysis to dynamically map the subqueries to their intermediate data, while also recognizing subqueries removed by the optimizers. For such subqueries, DeSQL efficiently regenerates the intermediate data from a nearby subquery’s data. On the popular TPC-DC benchmark, DeSQL provides a complete debugging view in 13% less time than the original job time while incurring an average overhead of 10% in addition to retaining Apache Spark’s scalability. In a user study comprising 15 participants engaged in two debugging tasks, we find that participants utilizing DeSQL identify the root cause behind a wrong query output in 74% less time than the de-facto, manual debugging. 

Student experiences in large undergraduate Computer Science courses are increasingly impacted by automated systems. Bots, or agents of software automation, are useful for efficiently grading and generating feedback. Current efforts at automation in CS education focus on supporting instructional tasks, but do not address student struggles due to poor behaviors, such as procrastination. In this paper, we explore using bots to improve the software engineering behaviors of students using developer recommendation choice architectures, a framework incorporating behavioral science concepts in recommendations to improve the actions of programmers. We implemented this framework in class-bot, a novel system designed to nudge students to make better choices while working on programming assignments. This work presents a preliminary evaluation integrating this tool in an introductory programming course. Our results show that class-bot is beneficial for improving student development behaviors increasing code quality and productivity. 

Recommendations between colleagues are effective for encouraging developers to adopt better practices. Research shows these peer interactions are useful for improving developer behaviors, or the adoption of activities to help software engineers complete programming tasks. However, in-person recommendations between developers in the workplace are declining. One form of online recommendations between developers are pull requests, which allow users to propose code changes and provide feedback on contributions. GitHub, a popular code hosting platform, recently introduced the suggested changes feature, which allows users to recommend improvements for pull requests. To better understand this feature and its impact on recommendations between developers, we report an empirical study of this system, measuring usage, effectiveness, and perception. Our results show that suggested changes support code review activities and significantly impact the timing and communication between developers on pull requests. This work provides insight into the suggested changes feature and implications for improving future systems for automated developer recommendations, such as providing situated, concise, and actionable feedback. 

Abstract Previous evolutionary reconstructions have concluded that early eukaryotic ancestors including both the last common ancestor of eukaryotes and of all fungi had intron-rich genomes. By contrast, some extant eukaryotes have few introns, underscoring the complex histories of intron–exon structures, and raising the question as to why these few introns are retained. Here, we have used recently available fungal genomes to address a variety of questions related to intron evolution. Evolutionary reconstruction of intron presence and absence using 263 diverse fungal species supports the idea that massive intron reduction through intron loss has occurred in multiple clades. The intron densities estimated in various fungal ancestors differ from zero to 7.6 introns per 1 kb of protein-coding sequence. Massive intron loss has occurred not only in microsporidian parasites and saccharomycetous yeasts, but also in diverse smuts and allies. To investigate the roles of the remaining introns in highly-reduced species, we have searched for their special characteristics in eight intron-poor fungi. Notably, the introns of ribosome-associated genes RPL7 and NOG2 have conserved positions; both intron-containing genes encoding snoRNAs. Furthermore, both the proteins and snoRNAs are involved in ribosome biogenesis, suggesting that the expression of the protein-coding genes and noncoding snoRNAs may be functionally coordinated. Indeed, these introns are also conserved in three-quarters of fungi species. Our study shows that fungal introns have a complex evolutionary history and underappreciated roles in gene expression.