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Memorias de investigación
Ponencias en congresos:
Experiments in Context-Sensitive Incremental and Modular Static Analysis in CiaoPP
Áreas de investigación
  • Ciencias de la computación y tecnología informática
Abstract interpretation is a widely used technique for automatically detecting errors and proving program properties related to correct- ness, security, cost, etc. Performing such analysis during software development helps in early bug detection, but, given the size and complex structure of real-life pro- grams, triggering a complete reanalysis for each set of changes is often too costly. However, development iterations normally involve small modifications in practice, which are often isolated within a small number of files or components. This can be taken advantage of to reduce the cost of re-analysis by reusing previous informa- tion. In particular, in CiaoPP [4, 2], incrementality-based cost reductions have been achieved to date at two levels: on one hand, modular context-sensitive analysis has been used to obtain global information on the whole program by iterating over local analyses of its components (modules). While this technique has been primarily aimed at reducing memory footprint, it can also achieve some incrementality. On the other hand, fine grain context-sensitive incremental analysis [3] identi- fies, invalidates, and recomputes only those parts of the analysis results that are affected by program changes. This analysis has been used to achieve very high levels of incrementality, with finer granularity (e.g., at program line level), but it does not take advantage of the module structure.
Nombre congreso
10th Workshop on Tools for Automatic Program Analysis
Tipo de participación
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Título de las actas
Esta actividad pertenece a memorias de investigación
  • Autor: Isabel García Contreras (IMDEA Software)
  • Autor: José Francisco Morales (IMDEA Software)
  • Autor: Manuel de Hermenegildo Salinas (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Computación lógica, Lenguajes, Implementación y Paralelismo (CLIP)
  • Departamento: Inteligencia Artificial
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