Completing and adapting models of biological processes

We present a learning-based method for model completion and adaptation, which is based on the combination of two approaches: 1) R2D2C, a technique for mechanically transforming system requirements via provably equivalent models to running code, and 2) automata learning-based model extrapolation. The intended impact of this new combination is to make model completion and adaptation accessible to experts of the field, like biologists or engineers. The principle is briefly illustrated by generating models of biological procedures concerning gene activities in the production of proteins, although the main application is going to concern autonomic systems for space exploration.1st IFIP International Conference on Biologically Inspired Cooperative Computing - Biological Inspiration 1Red de Universidades con Carreras en Informática (RedUNCI

Completing and Adapting Models of Biological Processes

We present a learning-based method for model completion and adaptation, which is based on the combination of two approaches: 1) R2D2C, a technique for mechanically transforming system requirements via provably equivalent models to running code, and 2) automata learning-based model extrapolation. The intended impact of this new combination is to make model completion and adaptation accessible to experts of the field, like biologists or engineers. The principle is briefly illustrated by generating models of biological procedures concerning gene activities in the production of proteins, although the main application is going to concern autonomic systems for space exploration

Completing and adapting models of biological processes

We present a learning-based method for model completion and adaptation, which is based on the combination of two approaches: 1) R2D2C, a technique for mechanically transforming system requirements via provably equivalent models to running code, and 2) automata learning-based model extrapolation. The intended impact of this new combination is to make model completion and adaptation accessible to experts of the field, like biologists or engineers. The principle is briefly illustrated by generating models of biological procedures concerning gene activities in the production of proteins, although the main application is going to concern autonomic systems for space exploration.1st IFIP International Conference on Biologically Inspired Cooperative Computing - Biological Inspiration 1Red de Universidades con Carreras en Informática (RedUNCI

Performance enhancements for a dynamic invariant detector

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 93-95).Dynamic invariant detection is the identification of the likely properties about a program based on observed variable values during program execution. While other dynamic invariant detectors use a brute force algorithm, Daikon adds powerful optimizations to provide more scalable invariant detection without sacrificing the richness of the reported invariants. Daikon improves scalability by eliminating redundant invariants. For example, the suppression optimization allows Daikon to delay the creation of invariants that are logically implied by other true invariants. Although conceptually simple, the implementation of this optimization in Daikon has a, large fixed cost and scales polynomially with the number of program variables. I investigated performance problems in two implementations of the suppression optimization in Daikon and evaluated several methods for improving the algorithm for the suppression optimization: optimizing existing algorithms, using a hybrid, context-sensitive approach to maximize the effectiveness of the two algorithms, and batching applications of the algorithm to lower costs. Experimental results showed a 10% runtime improvement in Daikon runtime. In addition, I implemented an oracle to verify the implementation of these improvements and the other optimizations in Daikon.by Chen Xiao.M.Eng

Checklistor i systemförvaltning - Kvalitetssäkring av användbarhet i utvecklingsprocessen

Checklistor för kvalitetssäkring i utvecklingsprocessen inom systemförvaltning är ett outforskat område. Problemet vi belyser är i vilka avseende checklistor fungerar som ett instrument för förbättrad användbarhet och kvalitet i förhållande till det som skall utvecklas. Vidare tittar vi på hur utvecklarna värderar hanteringen av checklistor. Vi har behandlat vilka dimensioner av systemförvaltning som checklistor är verksamma inom, på de olika företagen. Vi har tittat på vilket sätt utvecklarna värdesätter checklistor som verktyg för att säkerställa de krav som finns på användbarhet. Det vi har lyft fram i vår studie är hur utvecklare värderar balansen mellan fördelar ett system för med sig, gentemot den mängd energi en användare måste lägga ner för att förstå systemet till fullo. Den empiriska studien resulterade i att samtliga informanter var överens om att checklistor ansågs vara ett verktyg för förbättrad kvalitet dock i olika avseende. Vi fann ingen direkt påverkan på att checklistor fungerade som ett instrument för förbättrad användbarhet. Det visade sig snarare att det inträffade indirekt. I vissa avseenden, framför allt prioritering, struktur och lärande, ansåg vi finna kopplingar mellan användandet av checklistor, förenkling och hantering i/av utvecklingsprocessen. Interaktionen mellan checklista och utvecklingsprocessen påverkar i sin tur även användbarhetsaspekterna därmed det indirekta inträffandet

A Technique for Verifying Component-Based Software

Component-based software systems raise new problems for the testing community: the reuse of components suggests the possibility of reducing testing costs by reusing information about the quality of the software components. This paper addresses the problem of testing evolving software systems, i.e., systems obtained by modifying and/or substituting some of their components. The paper proposes a technique to automatically identify behavioral di#erences between di#erent versions of the system, to deduce possible problems from inconsistent behaviors. The approach is based on the automatic distilling of invariants from in-field executions. The computed invariants are used to monitor the behavior of new components, and to reveal unexpected interactions. The event generated while monitoring system executions are presented to software engineers who can infer possible problems of the new versions