Abstract Assume that a graph models a detection system for a facility with a possible “intruder,” or a multiprocessor network with a possible malfunctioning processor. We consider the problem of placing detectors at a subset of vertices in to determine the location of an intruder if there is any. Many types of detection systems have been defined for different sensor capabilities; in particular, we focus on identifying codes, where each detector can determine whether there is an intruder within its closed neighborhood. In this research we explore a fault‐tolerant variant of identifying codes applicable to real‐world systems. Specifically, error‐detecting identifying codes permit a false‐negative transmission from any single detector. We investigate minimum‐sized error‐detecting identifying codes in several classes of graphs, including cubic graphs and infinite grids, and show that the problem of determining said minimum size in arbitrary graphs is NP‐complete.

The ability to design an algorithm is one of the most important learning outcomes of a computer science program. Unfortunately, not only is learning how to design algorithms a challenging task, but many students believe that algorithm design is not an important part of problem solving. To address this challenge and hopefully change student attitudes, we developed AlgoTutor, a web-based algorithm development tutoring system. AlgoTutor's primary components are the algorithm composer and the algorithm tracer. A third component, ProgramPad, was added to show the connection between algorithms and code. This paper presents the results of experiments that assessed AlgoTutor's effectiveness in changing student attitudes about algorithm development. The results show that students who used AlgoTutor in CS-I were more likely to realize the importance of algorithm design in problem solving and to have confidence in their own algorithm development abilities.