Inappropriate use of software complexity measures can have large, damaging effects by rewarding poor programming practices and demoralizing good programmers. Software complexity measures must be critically evaluated to determine the ways in which they can best be used.

Fault localization in program debugging is the process of identifying program statements which cause anomalous behavior.We have developed a prototype, knowledge-based model of the fault localization process.Novel features of the model include multiple localization tactics and a recognition-based mechanism for program abstraction~ An explicit division of knowledge from the applications, programming and language domains facilitate model tuning within as well as across applications domains.We describe model structure and performance for a class of faults associated with master file update programs.We foresee applications of the model as an initial cognitive theory of expertise in fault localization and as a partially automated debugging tool.

Lkng the Hamming metric to compute distances between chromosomes values as bits, we introduce two real-valued measures in genetic algorithms. These measurq gestetk drift and chtstering, give insight into the behavioral effects of mutation convergence and mating (using simple crossover) during generation cycles. The genetic driti and cltsstsring messttres will then be appfied to two cmtmton computes network problems: the optimaf placement of a multi-station access unit (Steiner Point variation problem) ad path loss (edge integrity of a graph). hstroduction and DefinitionsGenetic algorithms are a class of search algorithms baaed on the mechanics of naturtd biological selection [7]. A data structure is created which reprraertta en individual trial in the search space of all pssible feasible solutions. Each individual in the population has a fitness value which ia an evaluation of the worth of that individual as a solution.In one of our applications, we use chromosomes of bit strings for our population members to daermine phtce.men~inter-connectednesa, and the total length of connections in a multistation access uniL We reward poptdation members with the smaller total length of connectims by giving them a higher fitness vafue and select them for breeding more often. Offspring are created by applying (probabilistic) chromosomaf crossover to two members (parents) during a breeding (generation). Offspring from such a union may be further modficd by random mutations of bits.A common difficulty with genetic algorithms is fme tuning the algorithm to prevent premature convergence, that is, convergence to local minima or maxima without adequate exploration of the search apace. The art of fine tuning genetic algorithms in unique applications is explored m [3], while a fuller description of genetic algorithms CS!Sbe found in Goldberg's book [7].We assume that the genetic algorithm ppulatkn consists of individual chromasorsres (bitmaps} O = fak, 1 = true. We use the following parameters to tune our genetic algorithsrw Name * Popx size of the poptdatiorL number of chromosomes N uniform length of the chromosomes in the GA population HamDiat Hamrning distance betweest two chromosomes The Hamming dMsnce (HamDtat) between two individuala in such a population is the number of corresponding bit kations where they differ: HasuDi.!7t(P1, P2) = $ PI [k] xor Pa[k] where P][k] and P2[k] is the kth blt vafue of the first and second member (respectively) of the pair. Using the Hamrning metric, we define genetk drift as the avemge distance of all individuals in the currentgeneration from the beat (most fit) population member found to date, similar to that found in [4]. mHtunDfst(P,, 13est) Genetic Drift = Pepsi ze Clustering ia the average pair-wise dmtance of all individuals in the went generation. m:wl WM E ,2Hesrtvist(Pk, P,) c1 us tering = ( Popsize 2 )The &nominator is the number of pairs of individuals in a ppulation of size Popsize. In both formulas, P, is the ids individual (chromosome) of theO 1W3ACM O-S979t-%$2#3ANXt2Mt2&..tl3 126

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Fault localization in program debugging is the process of identifying program statements which cause anomalous behavior.We have developed a prototype, knowledge-based model of the fault localization process.Novel features of the model include multiple localization tactics and a recognition-based mechanism for program abstraction~ An explicit division of knowledge from the applications, programming and language domains facilitate model tuning within as well as across applications domains.We describe model structure and performance for a class of faults associated with master file update programs.We foresee applications of the model as an initial cognitive theory of expertise in fault localization and as a partially automated debugging tool.

A primary focus in nursing education is to provide students with a diverse range of clinical experiences. Historically, the collection and assessment of data from students' clinical experiences have been paper-and-pencil tasks that are arduous for both students and nursing faculty. The volume of collected information also has made it difficult to produce ad hoc statistical reports without additional intensive manual labor. To facilitate recording and analysis of these data, the Nursing and Computer Science Departments at Indiana University-Purdue University Fort Wayne have collaborated to create a Web application: Essential Clinical Behaviors. The use of the Web-accessible database represents a major change in nursing education by alteration of format used by students to record their clinical experiences in nursing courses. The application was designed to enhance nursing students' learning and to assist faculty in making student assignments, evaluating student progress, and supporting curriculum decisions. This report discusses the rationale for the development of the Web application, a description of its data entry and reporting mechanisms, an overview of the system architecture, its use in the nursing curriculum, and planned enhancements.

Fault localization in program debugging is the process of identifying program statements which cause anomalous behaviour. We have developed a prototype, knowledge-based model of the fault localization process. Novel features of the model include multiple localization tactics and a recognition-based mechanism for program abstraction. An explicit division of knowledge from the applications, programming and language domains facilitate model tuning within as well as across applications domains. We describe model structure and performance for a class of faults associated with master file update programs. We foresee applications of the model as an initial cognitive theory of expertise in fault localization and as a partially automated debugging tool.

A controlled experiment comparing the first-year approaches of in-depth study of programming methodology to breadth of experience in programming and languages is described.The experiment involves two groups of students who will follow different paths in the freshman year which converge in the sophomore year.Performance of students from each group in the common sophomore courses will be analyzed.The efforts to reduce bias and the methods of measurement and analysis are described in detail.