Swarm robotics collectively solve problems that are challenging for individual robots, from environmental monitoring to entertainment. The algorithms enabling swarms allow individual robots of the swarm to plan, share, and coordinate their trajectories and tasks to achieve a common goal. Such algorithms rely on a large number of configurable parameters that can be tailored to target particular scenarios. This large configuration space, the complexity of the algorithms, and the dependencies with the robots’ setup and performance make debugging and fixing swarms configuration bugs extremely challenging. This paper proposes Swarmbug, a swarm debugging system that automatically diagnoses and fixes buggy behaviors caused by misconfiguration. The essence of Swarmbug is the novel concept called the degree of causal contribution (Dcc), which abstracts impacts of environment configurations (e.g., obstacles) to the drones in a swarm via behavior causal analysis. Swarmbug automatically generates, validates, and ranks fixes for configuration bugs. We evaluate Swarmbug on four diverse swarm algorithms. Swarmbug successfully fixes four configuration bugs in the evaluated algorithms, showing that it is generic and effective. We also conduct a real-world experiment with physical drones to show the Swarmbug’s fix is effective in the real-world.

The practical art of constructing database management systems (DBMSs) involves a morass of trade-offs among query execution speed, query optimization speed, standards compliance, feature parity, modularity, portability, and other goals. It is no surprise that DBMSs, like all complex software systems, contain bugs that can adversely affect their performance. The performance of DBMSs is an important metric as it determines how quickly an application can take in new information and use it to make new decisions. Both developers and users face challenges while dealing with performance regression bugs. First, developers usually find it challenging to manually design test cases to uncover performance regressions since DBMS components tend to have complex interactions. Second, users encountering performance regressions are often unable to report them, as the regression-triggering queries could be complex and database-dependent. Third, developers have to expend a lot of effort on localizing the root cause of the reported bugs, due to the system complexity and software development complexity. Given these challenges, this paper presents the design of Apollo, a toolchain for automatically detecting, reporting, and diagnosing performance regressions in DBMSs. We demonstrate that Apollo automates the generation of regression-triggering queries, simplifies the bug reporting process for users, and enables developers to quickly pinpoint the root cause of performance regressions. By automating the detection and diagnosis of performance regressions, Apollo reduces the labor cost of developing efficient DBMSs.