Learning Tractable Probabilistic Models for Fault Localization

In recent years, several probabilistic techniques have been applied to various debugging problems. However, most existing probabilistic debugging systems use relatively simple statistical models, and fail to generalize across multiple programs. In this work, we propose Tractable Fault Localization Models (TFLMs) that can be learned from data, and probabilistically infer the location of the bug. While most previous statistical debugging methods generalize over many executions of a single program, TFLMs are trained on a corpus of previously seen buggy programs, and learn to identify recurring patterns of bugs. Widely-used fault localization techniques such as TARANTULA evaluate the suspiciousness of each line in isolation; in contrast, a TFLM defines a joint probability distribution over buggy indicator variables for each line. Joint distributions with rich dependency structure are often computationally intractable; TFLMs avoid this by exploiting recent developments in tractable probabilistic models (specifically, Relational SPNs). Further, TFLMs can incorporate additional sources of information, including coverage-based features such as TARANTULA. We evaluate the fault localization performance of TFLMs that include TARANTULA scores as features in the probabilistic model. Our study shows that the learned TFLMs isolate bugs more effectively than previous statistical methods or using TARANTULA directly.Comment: Fifth International Workshop on Statistical Relational AI (StaR-AI 2015

Dissection of a Bug Dataset: Anatomy of 395 Patches from Defects4J

Well-designed and publicly available datasets of bugs are an invaluable asset to advance research fields such as fault localization and program repair as they allow directly and fairly comparison between competing techniques and also the replication of experiments. These datasets need to be deeply understood by researchers: the answer for questions like "which bugs can my technique handle?" and "for which bugs is my technique effective?" depends on the comprehension of properties related to bugs and their patches. However, such properties are usually not included in the datasets, and there is still no widely adopted methodology for characterizing bugs and patches. In this work, we deeply study 395 patches of the Defects4J dataset. Quantitative properties (patch size and spreading) were automatically extracted, whereas qualitative ones (repair actions and patterns) were manually extracted using a thematic analysis-based approach. We found that 1) the median size of Defects4J patches is four lines, and almost 30% of the patches contain only addition of lines; 2) 92% of the patches change only one file, and 38% has no spreading at all; 3) the top-3 most applied repair actions are addition of method calls, conditionals, and assignments, occurring in 77% of the patches; and 4) nine repair patterns were found for 95% of the patches, where the most prevalent, appearing in 43% of the patches, is on conditional blocks. These results are useful for researchers to perform advanced analysis on their techniques' results based on Defects4J. Moreover, our set of properties can be used to characterize and compare different bug datasets.Comment: Accepted for SANER'18 (25th edition of IEEE International Conference on Software Analysis, Evolution and Reengineering), Campobasso, Ital

Exploiting Spatial Code Proximity and Order for Improved Source Code Retrieval for Bug Localization

Abstract—Practically all Information Retrieval (IR) based approaches developed to date for automatic bug localization are based on the bag-of-words assumption that ignores any positional and ordering relationships between the terms in a query. In this paper we argue that bug reports are ill-served by this assumption since such reports frequently contain various types of structural information whose terms must obey certain positional and ordering constraints. It therefore stands to reason that the quality of retrieval for bug localization would improve if these constraints could be taken into account when searching for the most relevant files. In this paper, we demonstrate that such is indeed the case. We show how the well-known Markov Random Field (MRF) based retrieval framework can be used for taking into account the term-term proximity and ordering relationships in a query vis-a-vis the same relationships in the files of a source-code library to greatly improve the quality of retrieval of the most relevant source files. We have carried out our experimental evaluations on popular large software projects using over 4 thousand bug reports. The results we present demonstrate unequivocally that the new proposed approach is far superior to the widely used bag-of-words based approaches

Source Code Retrieval from Large Software Libraries for Automatic Bug Localization

This dissertation advances the state-of-the-art in information retrieval (IR) based approaches to automatic bug localization in software. In an IR-based approach, one first creates a search engine using a probabilistic or a deterministic model for the files in a software library. Subsequently, a bug report is treated as a query to the search engine for retrieving the files relevant to the bug. With regard to the new work presented, we first demonstrate the importance of taking version histories of the files into account for achieving significant improvements in the precision with which the files related to a bug are located. This is motivated by the realization that the files that have not changed in a long time are likely to have ``stabilized and are therefore less likely to contain bugs. Subsequently, we look at the difficulties created by the fact that developers frequently use abbreviations and concatenations that are not likely to be familiar to someone trying to locate the files related to a bug. We show how an initial query can be automatically reformulated to include the relevant actual terms in the files by an analysis of the files retrieved in response to the original query for terms that are proximal to the original query terms. The last part of this dissertation generalizes our term-proximity based work by using Markov Random Fields (MRF) to model the inter-term dependencies in a query vis-a-vis the files. Our MRF work redresses one of the major defects of the most commonly used modeling approaches in IR, which is the loss of all inter-term relationships in the documents

Computer Aided Verification

The open access two-volume set LNCS 12224 and 12225 constitutes the refereed proceedings of the 32st International Conference on Computer Aided Verification, CAV 2020, held in Los Angeles, CA, USA, in July 2020.* The 43 full papers presented together with 18 tool papers and 4 case studies, were carefully reviewed and selected from 240 submissions. The papers were organized in the following topical sections: Part I: AI verification; blockchain and Security; Concurrency; hardware verification and decision procedures; and hybrid and dynamic systems. Part II: model checking; software verification; stochastic systems; and synthesis. *The conference was held virtually due to the COVID-19 pandemic

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions