Abstract Software model checkers are able to exhaustively explore different bounded program executions arising from various sources of non-determinism. These tools provide statements to produce non-deterministic values for certain variables, thus forcing the corresponding model checker to considerallpossible values for these during verification. While these statements offer an effective way of verifying programs handling basic data types and simple structured types, they are inappropriate as a mechanism for nondeterministic generation of pointers, favoring the use of insertion routines to produce dynamic data structures when verifying, via model checking, programs handling such data types. We present a technique to improve model checking of programs handling heap-allocated data types, by taming the explosion of candidate structures that can be built when non-deterministically initializing heap object fields. The technique exploits precomputedrelational bounds, that disregard values deemed invalid by the structure’s type invariant, thus reducing the state space to be explored by the model checker. Precomputing the relational bounds is a challenging costly task too, for which we also present an efficient algorithm, based on incremental SAT solving. We implement our approach on top of the bounded model checker, and show that, for a number of data structures implementations, we can handle significantly larger input structures and detect faults that is unable to detect.

In goal-oriented requirements engineering approaches, conflict analysis has been proposed as an abstraction for risk analysis. Intuitively, given a set of expected goals to be achieved by the system-to-be, a conflict represents a subtle situation that makes goals diverge, i.e., not be satisfiable as a whole. Conflict analysis is typically driven by the identify-assess-control cycle, aimed at identifying, assessing and resolving conflicts that may obstruct the satisfaction of the expected goals. In particular, the assessment step is concerned with evaluating how likely the identified conflicts are, and how likely and severe are their consequences.