Smelling faults in spreadsheets

Despite being staggeringly error prone, spreadsheets are a highly flexible programming environment that is widely used in industry. In fact, spreadsheets are widely adopted for decision making, and decisions taken upon wrong (spreadsheet-based) assumptions may have serious economical impacts on businesses, among other consequences. This paper proposes a technique to automatically pinpoint potential faults in spreadsheets. It combines a catalog of spreadsheet smells that provide a first indication of a potential fault, with a generic spectrum-based fault localization strategy in order to improve (in terms of accuracy and false positive rate) on these initial results. Our technique has been implemented in a tool which helps users detecting faults.To validate the proposed technique, we consider a wellknown and well-documented catalog of faulty spreadsheets. Our experiments yield two main results: we were able to distinguish between smells that can point to faulty cells from smells and those that are not capable of doing so; and we provide a technique capable of detecting a significant number of errors: two thirds of the cells labeled as faulty are in fact (documented) errors

FaultySheet detective: when smells meet fault localization

This paper presents a tool, dubbed FaultySheet Detective, for aiding in spreadsheet fault localization, which combines the detection of bad smells with a generic spectrum-based fault localization algorithm

MintHint: Automated Synthesis of Repair Hints

Being able to automatically repair programs is an extremely challenging task. In this paper, we present MintHint, a novel technique for program repair that is a departure from most of today's approaches. Instead of trying to fully automate program repair, which is often an unachievable goal, MintHint performs statistical correlation analysis to identify expressions that are likely to occur in the repaired code and generates, using pattern-matching based synthesis, repair hints from these expressions. Intuitively, these hints suggest how to rectify a faulty statement and help developers find a complete, actual repair. MintHint can address a variety of common faults, including incorrect, spurious, and missing expressions. We present a user study that shows that developers' productivity can improve manyfold with the use of repair hints generated by MintHint -- compared to having only traditional fault localization information. We also apply MintHint to several faults of a widely used Unix utility program to further assess the effectiveness of the approach. Our results show that MintHint performs well even in situations where (1) the repair space searched does not contain the exact repair, and (2) the operational specification obtained from the test cases for repair is incomplete or even imprecise

Design and Analysis of Novel Kernel Measure for Software Fault Localization

The problem of software fault localization may be viewed as an approach for finding hidden faults or bugs in the existing program codes which are syntactically correct and give fault free output for some input instances but fail for all other input instances. Some of the reasons include logical errors, wrong interpretation of specification, coding errors. Finding such faults is not possible sometimes with the help of compilers. This is where the necessity and significance of software fault localization stems out. The main contribution for this work is to first introduce the block hit-miss function which relates block vectors of execution sequences of software code over sample runs performed and the decision vector which denotes fault or error free output. The similarity measure is applied to the block vector and decision vectors as input and the pair with maximum similarity is considered as faulty block.Comment: ICEMIS '15: Proceedings of the The International Conference on Engineering & MIS 201

Integration of Fault Localization into your GitHub Repository

Com a crescente complexidade e escala do software, existe uma forte necessidade de técnicas que auxiliem os desenvolvedores de software a localizar falhas com o mínimo de intervenção humana possível. O objetivo desta dissertação é analisar o uso de abordagens de localização de falhas baseadas em espectro para ajudar a descobrir falhas em programas Java, bem como o uso de bots no ciclo de vida do desenvolvimento de um software. As técnicas de localização de falhas baseadas em espectro foram escolhidas na área de pesquisa de localização de falhas de software devido aos seus baixos custos de execução e popularidade. Três ferramentas (GZoltar, FLACOCO e Jaguar) destacaram-se como as principais escolhas para a localização de falhas baseada em espectro em Java, de acordo com a pesquisa, e embora todas produzissem resultados comparáveis, o GZoltar foi preferido. Foi criada uma Action do GitHub que, quando integrada com o GZoltar, permite a análise de relatórios de localização de falhas baseada em espectro em qualquer repositório Java no GitHub. O resultado é um relatório detalhado das linhas de código potencialmente com falhas, personalizável pelo utilizador. Esta Action foi avaliada tanto em um repositório de exemplo como em vários projetos open-source. Embora a integração tenha sido bem sucedida no repositório de exemplo, as limitações do GZoltar impedem a sua integração na maioria dos projetos open-source, destacando a necessidade de atualizações e testes adicionais de compatibilidade.With the increased complexity and scale of software, there is a strong demand for techniques to guide software engineers to locate faults with less human intervention as possible. The purpose of this dissertation is to look into the usage of Spectrum-based Fault Localization approaches to help discover faults in Java programs, as well as the use of bots in the software development lifecycle. Spectrum-based Fault Localization techniques were found to be chosen in the research area of software fault localization due to their low execution costs and popularity. Three tools (GZoltar, FLACOCO, and Jaguar) stood out as the top choices for spectrum-based fault localization in Java according to the research, and even though all produced comparable outcomes, GZoltar was preferred. A GitHub Action was created that, when integrated with GZoltar, allows analysis of Spectrum-based Fault Localization reports in any Java repository on GitHub. The outcome of it is a detailed report of potentially faulty lines of code, customizable by the user. This action is tested in both a sample repository and several open-source projects. While successful integration is achieved with the sample repository, limitations of GZoltar hinder its integration with most open-source projects, highlighting the need for updates and further compatibility testing

Deep Learning Software Repositories

Bridging the abstraction gap between artifacts and concepts is the essence of software engineering (SE) research problems. SE researchers regularly use machine learning to bridge this gap, but there are three fundamental issues with traditional applications of machine learning in SE research. Traditional applications are too reliant on labeled data. They are too reliant on human intuition, and they are not capable of learning expressive yet efficient internal representations. Ultimately, SE research needs approaches that can automatically learn representations of massive, heterogeneous, datasets in situ, apply the learned features to a particular task and possibly transfer knowledge from task to task. Improvements in both computational power and the amount of memory in modern computer architectures have enabled new approaches to canonical machine learning tasks. Specifically, these architectural advances have enabled machines that are capable of learning deep, compositional representations of massive data depots. The rise of deep learning has ushered in tremendous advances in several fields. Given the complexity of software repositories, we presume deep learning has the potential to usher in new analytical frameworks and methodologies for SE research and the practical applications it reaches. This dissertation examines and enables deep learning algorithms in different SE contexts. We demonstrate that deep learners significantly outperform state-of-the-practice software language models at code suggestion on a Java corpus. Further, these deep learners for code suggestion automatically learn how to represent lexical elements. We use these representations to transmute source code into structures for detecting similar code fragments at different levels of granularity—without declaring features for how the source code is to be represented. Then we use our learning-based framework for encoding fragments to intelligently select and adapt statements in a codebase for automated program repair. In our work on code suggestion, code clone detection, and automated program repair, everything for representing lexical elements and code fragments is mined from the source code repository. Indeed, our work aims to move SE research from the art of feature engineering to the science of automated discovery