Faults in spreadsheets can represent a major risk for businesses. To minimize such risks, various automated testing and debugging approaches for spreadsheets were proposed. In such approaches, often one main assumption is that the spreadsheet developer is able to indicate if the outcomes of certain calculations correspond to the intended values. This, however, might require that the user performs calculations manually, a process which can easily become tedious and error-prone for more complex spreadsheets. In this work, we propose an interactive spreadsheet algorithmic debugging method, which is based on partitioning the spreadsheet into fragments. Test cases can then be automatically or manually created for each of these smaller fragments, whose correctness or faultiness can be easier assessed by users than test cases that cover the entire spreadsheet. The annotated test cases are then fed into an algorithmic debugging technique, which returns a set of formulas that could have caused any observed failures, i.e., discrepancies between the expected and computed calculation outcomes. Simulation experiments demonstrate that the suggested decomposition approach can speed up the algorithmic debugging process and significantly reduce the number of fault candidates returned by the algorithm. An additional laboratory study shows that fragmenting a spreadsheet with our method furthermore reduces the time needed by users for creating test cases for a spreadsheet.

Several cases are known where faults in spreadsheets have caused severe losses of money for companies. Besides other factors, the non-existence of advanced testing and debugging mechanisms in environments like MS Excel causes faults in spreadsheets to remain undetected. In this paper we describe the main functionality of the EXQUISITE tool, a software system and add-in to MS Excel that was developed in the context of our research on designing next-generation mechanisms for spreadsheet testing and debugging based on artificial intelligence technology. The tool in particular supports a novel algorithmic debugging approach and was successfully validated through different user studies.

Spreadsheets serve as a basis for decision-making processes in many companies and bugs in spreadsheets can therefore represent a considerable risk to businesses. Systematic tests can help to locate such bugs, but providing test cases can be cumbersome and complex for large real-world spreadsheets. To make the specification of test cases easier, we propose to split spreadsheets into smaller logically connected parts (called fragments) which can be individually tested for correctness. We present an algorithmic approach to compute such fragments, which we validated with a laboratory study in the form of a spreadsheet debugging exercise involving 57 subjects. The results show that the fragmentation approach can help to significantly reduce the required efforts to test a spreadsheet.

Spreadsheet environments like MS Excel are the most widespread type of end-user software development tools and spreadsheet-based applications can be found almost everywhere in organizations. Since spreadsheets are prone to error, several approaches were proposed in the research literature to help users locate formula errors. However, the proposed methods were often designed based on assumptions about the nature of errors and were evaluated with mutations of correct spreadsheets. In this work we propose a method and tool to identify realworld formula errors within the Enron spreadsheet corpus. Our approach is based on heuristics that help us identify versions of the same spreadsheet and our software helps the user identify spreadsheets of which we assume that they contain error corrections. An initial manual inspection of a subset of such candidates led to the identification of more than two dozen formula errors. We publicly share the new collection of real-world spreadsheet errors.

Model-Based Diagnosis (MBD) is a principled and domain-independent way of analyzing why a system under examination is not behaving as expected. Given an abstract description (model) of the system's components and their behavior when functioning normally, MBD techniques rely on observations about the actual system behavior to reason about possible causes when there are discrepancies between the expected and observed behavior. Due to its generality, MBD has been successfully applied in a variety of application domains over the last decades. In many application domains of MBD, testing different hypotheses about the reasons for a failure can be computationally costly, e.g., because complex simulations of the system behavior have to be performed. In this work, we therefore propose different schemes of parallelizing the diagnostic reasoning process in order to better exploit the capabilities of modern multi-core computers. We propose and systematically evaluate parallelization schemes for Reiter's hitting set algorithm for finding all or a few leading minimal diagnoses using two different conflict detection techniques. Furthermore, we perform initial experiments for a basic depth-first search strategy to assess the potential of parallelization when searching for one single diagnosis. Finally, we test the effects of parallelizing "direct encodings" of the diagnosis problem in a constraint solver.

Model-Based Diagnosis techniques have been successfully applied to support a variety of fault-localization tasks both for hardware and software artifacts. In many applications, Reiter's hitting set algorithm has been used to determine the set of all diagnoses for a given problem. In order to construct the diagnoses with increasing cardinality, Reiter proposed a breadth-first search scheme in combination with different tree-pruning rules. Since many of today's computing devices have multi-core CPU architectures, we propose techniques to parallelize the construction of the tree to better utilize the computing resources without losing any diagnoses. Experimental evaluations using different benchmark problems show that parallelization can help to significantly reduce the required running times. Additional simulation experiments were performed to understand how the characteristics of the underlying problem structure impact the achieved performance gains.