In this paper, we exploit a new multi-country historical dataset on public (government) debt to search for a systemic relationship between high public debt level growth and inflation our main result is that whereas the link between growth and debt seems relatively weak at “normal” debt levels, median growth rates for countries with over roughly ninety percent of gdp are about one percent lower than otherwise; average (mean) growth rates are several percent lower . surprisingly,

Growth in a Time of Debt

The following three articles are full versions of extended abstracts that were presented at the Twenty-Third ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS) held in Paris, France, June 14–16, 2004. After the conference, the authors were invited to submit full versions of their papers for publication in JACM. The articles have been reviewed according to the standard JACM refereeing process. We wish to thank the PODS Program Committee for their help in selecting these papers.

Journal of the ACM

The use of spreadsheets to capture information is widespread in industry. Spreadsheets can thus be a wealthy source of domain information. We propose to automatically extract this information and transform it into class diagrams. The resulting class diagram can be used by software engineers to understand, refine, or re-implement the spreadsheet’s functionality. To enable the transformation into class diagrams we create a library of common spreadsheet usage patterns. These patterns are localized in the spreadsheet using a two- dimensional parsing algorithm. The resulting parse tree is transformed and enriched with information from the library. We evaluate our approach on the spreadsheets from the Euses Spreadsheet Corpus by comparing a subset of the generated class diagrams with reference class diagrams created manually. Preprint accepted for publication in Proceedings of the 24th European Conference on Object-Oriented Programming (ECOOP 2010), Maribor (Slovenija), 21-25 June 2010, Lecture Notes in Computer Science, Springer-Verlag, 2010.

/pdf/automatically-extracting-class-diagrams-from-spreadsheets-ospetdcdwu.pdf

Automatically extracting class diagrams from spreadsheets

Spreadsheets are often used in business, for simple tasks, as well as for mission critical tasks such as finance or forecasting. Similar to software, some spreadsheets are of better quality than others, for instance with respect to usability, maintainability or reliability. In contrast with software however, spreadsheets are rarely checked, tested or certified. In this paper, we aim at developing an approach for detecting smells that indicate weak points in a spreadsheet’s design. To that end we first study code smells and transform these code smells to their spreadsheet counterparts. We then present an approach to detect the smells, and communicate located smells to spreadsheet users with data flow diagrams. We analyzed occurrences of these smells in the Euses corpus. Furthermore we conducted ten case studies in an industrial setting. The results of the evaluation indicate that smells can indeed reveal weaknesses in a spreadsheet’s design, and that data flow diagrams are an appropriate way to show those weaknesses. Paper presented at: ICSE 2012, the 34th International Conference on Software Engineering, June 2-9, 2012 in Zurich, Switzerland.

Detecting and Visualizing Inter-Worksheet Smells in Spreadsheets

Interactive 광고에 대한 연구

Spreadsheet programs are probably the most successful example of end-user software development tools and are used for a variety of purposes. Like any type of software, they are prone to error, in particular as they are usually developed by non-programmers. While various techniques exist to support the developer in finding errors in procedural programs, the tool support for spreadsheet debugging is still limited. In this paper, we show how techniques from model-based diagnosis can be applied and extended for spreadsheet debugging by translating the relevant parts of a spreadsheet to a constraint satisfaction problem. We additionally propose both problem-specific and generalizable extensions to the classical diagnosis algorithms which help to detect potential problems in a spreadsheet based on user-provided test cases more efficiently. The proposed techniques were integrated into a modular framework for spreadsheet debugging and evaluated with respect to scalability based on a number of real-world and artificially created spreadsheets. An additional error detection exercise involving 24 subjects was performed to assess the general applicability of such advanced spreadsheet debugging techniques for end users.

Model-based diagnosis of spreadsheet programs: a constraint-based debugging approach

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 con flict 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.

/pdf/parallel-model-based-diagnosis-on-multi-core-computers-526igtrsim.pdf

Parallel model-based diagnosis on multi-core computers

The computation of minimal conflict sets is a central task when the goal is to find relaxations or explanations for overconstrained problem formulations and in particular in the context of Model-Based Diagnosis (MBD) approaches. In this paper we propose MERGEXPLAIN, a non-intrusive conflict detection algorithm which implements a divide-and-conquer strategy to decompose a problem into a set of smaller independent subproblems. Our technique allows us to efficiently determine multiple minimal conflicts during one single problem decomposition run, which is particularly helpful in MBD problem settings. An empirical evaluation on various benchmark problems shows that our method can lead to a significant reduction of the required diagnosis times.

/pdf/mergexplain-fast-computation-of-multiple-conflicts-for-f4y9cyl91p.pdf

MERGEXPLAIN: fast computation of multiple conflicts for diagnosis

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, Re-iter'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.

https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9329/9425

Parallelized hitting set computation for model-based diagnosis

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.1

A decomposition-based approach to spreadsheet testing and debugging

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