Domain-Type-Guided Refinement Selection Based on Sliced Path Prefixes

Abstraction is a successful technique in software verification, and interpolation on infeasible error paths is a successful approach to automatically detect the right level of abstraction in counterexample-guided abstraction refinement. Because the interpolants have a significant influence on the quality of the abstraction, and thus, the effectiveness of the verification, an algorithm for deriving the best possible interpolants is desirable. We present an analysis-independent technique that makes it possible to extract several alternative sequences of interpolants from one given infeasible error path, if there are several reasons for infeasibility in the error path. We take as input the given infeasible error path and apply a slicing technique to obtain a set of error paths that are more abstract than the original error path but still infeasible, each for a different reason. The (more abstract) constraints of the new paths can be passed to a standard interpolation engine, in order to obtain a set of interpolant sequences, one for each new path. The analysis can then choose from this set of interpolant sequences and select the most appropriate, instead of being bound to the single interpolant sequence that the interpolation engine would normally return. For example, we can select based on domain types of variables in the interpolants, prefer to avoid loop counters, or compare with templates for potential loop invariants, and thus control what kind of information occurs in the abstraction of the program. We implemented the new algorithm in the open-source verification framework CPAchecker and show that our proof-technique-independent approach yields a significant improvement of the effectiveness and efficiency of the verification process.Comment: 10 pages, 5 figures, 1 table, 4 algorithm

Combining k-Induction with Continuously-Refined Invariants

Bounded model checking (BMC) is a well-known and successful technique for finding bugs in software. k-induction is an approach to extend BMC-based approaches from falsification to verification. Automatically generated auxiliary invariants can be used to strengthen the induction hypothesis. We improve this approach and further increase effectiveness and efficiency in the following way: we start with light-weight invariants and refine these invariants continuously during the analysis. We present and evaluate an implementation of our approach in the open-source verification-framework CPAchecker. Our experiments show that combining k-induction with continuously-refined invariants significantly increases effectiveness and efficiency, and outperforms all existing implementations of k-induction-based software verification in terms of successful verification results.Comment: 12 pages, 5 figures, 2 tables, 2 algorithm

CrocoPat 2.1 Introduction and Reference Manual

CrocoPat is an efficient, powerful and easy-to-use tool for manipulating relations of arbitrary arity, including directed graphs. This manual provides an introduction to and a reference for CrocoPat and its programming language RML. It includes several application examples, in particular from the analysis of structural models of software systems.Comment: 19 pages + cover, 2 eps figures, uses llncs.cls and cs_techrpt_cover.sty, for downloading the source code, binaries, and RML examples, see http://www.software-systemtechnik.de/CrocoPat

Feature-Aware Verification

A software product line is a set of software products that are distinguished in terms of features (i.e., end-user--visible units of behavior). Feature interactions ---situations in which the combination of features leads to emergent and possibly critical behavior--- are a major source of failures in software product lines. We explore how feature-aware verification can improve the automatic detection of feature interactions in software product lines. Feature-aware verification uses product-line verification techniques and supports the specification of feature properties along with the features in separate and composable units. It integrates the technique of variability encoding to verify a product line without generating and checking a possibly exponential number of feature combinations. We developed the tool suite SPLverifier for feature-aware verification, which is based on standard model-checking technology. We applied it to an e-mail system that incorporates domain knowledge of AT&T. We found that feature interactions can be detected automatically based on specifications that have only feature-local knowledge, and that variability encoding significantly improves the verification performance when proving the absence of interactions.Comment: 12 pages, 9 figures, 1 tabl

Managerial ownership, entrenchment and innovation

Principle-agent theory suggests managers might under-invest into R&D for reasons of risk tied to project failure, such as reduced remuneration and job loss. However, managers might over-invest into innovation for reasons of growth implying higher remuneration, power and prestige. Using a sample of 1,406 Belgian firms, we find, first, that managers holding no company shares under-invest into R&D compared to owners giving rise to the risk argument. Second, we find an inverse u-shaped relationship between the degree of managerial ownership and R&D. Thus, managers become entrenched, i.e. powerful enough to pursue their own interests. When entrenched, managers do not fear detrimental effects of risky innovation projects on their career, and hence tend to over-invest into innovation. --corporate governance,managerial ownership,entrenchment,innovation,R&D investments

Managerial ownership, entrenchment and innovation.

Principle-agent theory suggests managers might under-invest into R&D for reasons of risk tied to project failure, such as reduced remuneration and job loss. However, managers might over-invest into innovation for reasons of growth implying higher remuneration, power and prestige. Using a sample of 1,406 Belgian firms, we find, first, that managers holding no company shares under-invest into R&D compared to owners giving rise to the risk argument. Second, we find an inverse u-shaped relationship between the degree of managerial ownership and R&D. Thus, managers become entrenched, i.e. powerful enough to pursue their own interests. When entrenched, managers do not fear detrimental effects of risky innovation projects on their career, and hence tend to over-invest into innovation.corporate governance; managerial ownership; entrenchment; innovation; R&D investments;