Software fault localization, the act of identifying the locations of faults in a program, is widely recognized to be one of the most tedious, time consuming, and expensive – yet equally critical – activities in program debugging. Due to the increasing scale and complexity of software today, manually locating faults when failures occur is rapidly becoming infeasible, and consequently, there is a strong demand for techniques that can guide software developers to the locations of faults in a program with minimal human intervention. This demand in turn has fueled the proposal and development of a broad spectrum of fault localization techniques, each of which aims to streamline the fault localization process and make it more effective by attacking the problem in a unique way. In this article, we catalog and provide a comprehensive overview of such techniques and discuss key issues and concerns that are pertinent to software fault localization as a whole.

A Survey on Software Fault Localization

Recently, block-based programming languages like Alice, Scratch and Blockly have become popular tools for programming education. There is substantial research showing that block-based languages are suitable for early programming education. But can block-based programs be smelly too? And does that matter to learners? In this paper we explore the code smells metaphor in the context of block-based programming language Scratch. We conduct a controlled experiment with 61 novice Scratch programmers, in which we divided the novices into three groups. One third receive a non-smelly program, while the other groups receive a program suffering from the Duplication or the Long Method smell respectively. All subjects then perform the same comprehension tasks on their program, after which we measure their time and correctness. The results of the experiment show that code smell indeed influence performance: subjects working on the program exhibiting code smells perform significantly worse, but the smells did not affect the time subjects needed. Investigating different types of tasks in more detail, we find that Long Method mainly decreases system understanding, while Duplication decreases the ease with which subjects modify Scratch programs.

Do code smells hamper novice programming? A controlled experiment on Scratch programs

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

The number of end-users who write spreadsheet programs is at least an order of magnitude larger than the number of trained programmers who write professional software. We studied a corpus of 3691 spreadsheets and we found that their formulas are riddled with the same smells that plague professional software: hardcoded constants, duplicated expressions, unnecessary complexity, and unsanitized input. These make spreadsheets difficult to read and expensive to maintain. Like automated refactoring in the object-oriented domain, spreadsheet refactoring can be transformative. In this paper we present seven refactorings for spreadsheet formulas implemented in RefBook, a plugin for Microsoft Excel. To evaluate the usefulness of RefBook, we employed three kinds of empirical methods. First, we conducted a User Survey with 28 Excel users to find out whether they preferred the refactored formulas. Second, we conducted a Controlled Experiment with the same 28 participants to measure their productivity when doing manual refactorings. Third, we performed a Retrospective Case Study on the EUSES Spreadsheet Corpus with 3691 spreadsheets to determine how often we could apply the refactorings supported by RefBook. The results show: (i) users prefer the improved quality of refactored formulas, (ii) RefBook is faster and more reliable than manual refactoring, and (iii) the refactorings are widely applicable. On average RefBook is able to apply the refactorings in less than half the time that users performed the refactorings manually. 92.54% of users introduced errors or new smells into the spreadsheet or were unable to complete the task.

/pdf/refactoring-meets-spreadsheet-formulas-5audidhjb9.pdf

Refactoring meets spreadsheet formulas

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

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

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Detecting and Visualizing Inter-Worksheet Smells in Spreadsheets