While contemporary projectional editors make sure that the edited programs conform to the programming language's metamodel, they do not enforce that they are also well-formed, that is, that they obey the well-formedness rules defined for the language. We show how, based on a constraint-based capture of well-formedness, projectional editors can be empowered to enforce well-formedness in much the same way they enforce conformance with the metamodel. The resulting robust edits may be more complex than ordinary, well-formedness breaking edits, and hence may require more user involvement; yet, maintaining well-formedness at all times ensures that necessary corrections of a program are linked to the edit that necessitated them, and that the projectional editor's services are never compromised by inconsistent programs. Robust projectional editing is not a straitjacket, however: If a programmer prefers to work without it, its constraint-based capture of well-formedness will still catch all introduced errors - unlike many other editor services, well-formedness checking and robust editing are based on the same implementation, and are hence guaranteed to behave consistently.

Programs may contain multiple faults, in which case their debugging can be parallelized. However, effective parallelization requires some guarantees that parallel debugging tasks do not address the same fault, an inherent problem of earlier, clustering-based approaches to parallel debugging. In this paper, we identify a number of fundamental trade-offs to be made when selecting algorithms for parallel debugging, and explore these trade-offs using one clustering algorithm and three algorithms from integer linear programming. Results of an evaluation involving a total of 75,000 faulty versions (with up to 32 injected faults) of 15 subject programs suggest that depending on the number of faults present and the trade-offs one is willing to accept, speed-ups much larger than previously reported can be achieved, even if all derived parallel debugging tasks are handled sequentially.

Resuming past work on coverage-based fault localization, we find that empirical assessments of its accuracy are subject to so many imponderables that they are of limited value. To improve on this situation, we have compiled a comprehensive list of threats to be considered when attempting such assessments in the future. In addition, we propose the establishment of theoretical lower and upper bounds of fault localization accuracy that depend on properties of the subject programs (including their test suites) only. We make a suggestion for a lower bound and show that well-known fault locators do not uniformly perform better.

Coverage-based fault localization extends the utility of testing from detecting the presence of faults to their localization. While coverage-based fault localization has shown good evaluation results for the single fault case, its ability to localize several faults at once appears to be limited. In this paper, we show how two partitioning procedures borrowed from integer linear programming can help improve the accuracy of standard coverage-based fault locators in presence of multiple faults by breaking down the localization problem into several smaller ones that can be dealt with independently. Experimental results suggest that our approach is indeed useful, the more so as its cost appears to be negligible.