SMT-Based Refutation of Spurious Bug Reports in the Clang Static Analyzer

We describe and evaluate a bug refutation extension for the Clang Static Analyzer (CSA) that addresses the limitations of the existing built-in constraint solver. In particular, we complement CSA's existing heuristics that remove spurious bug reports. We encode the path constraints produced by CSA as Satisfiability Modulo Theories (SMT) problems, use SMT solvers to precisely check them for satisfiability, and remove bug reports whose associated path constraints are unsatisfiable. Our refutation extension refutes spurious bug reports in 8 out of 12 widely used open-source applications; on average, it refutes ca. 7% of all bug reports, and never refutes any true bug report. It incurs only negligible performance overheads, and on average adds 1.2% to the runtime of the full Clang/LLVM toolchain. A demonstration is available at {\tt https://www.youtube.com/watch?v=ylW5iRYNsGA}.Comment: 4 page

Poster: Improving Bug Localization with Report Quality Dynamics and Query Reformulation

Recent findings from a user study suggest that IR-based bug localization techniques do not perform well if the bug report lacks rich structured information such as relevant program entity names. On the contrary, excessive structured information such as stack traces in the bug report might always not be helpful for the automated bug localization. In this paper, we conduct a large empirical study using 5,500 bug reports from eight subject systems and replicating three existing studies from the literature. Our findings (1) empirically demonstrate how quality dynamics of bug reports affect the performances of IR-based bug localization, and (2) suggest potential ways (e.g., query reformulations) to overcome such limitations.Comment: The 40th International Conference on Software Engineering (Companion volume, Poster Track) (ICSE 2018), pp. 348--349, Gothenburg, Sweden, May, 201

E-QED: Electrical Bug Localization During Post-Silicon Validation Enabled by Quick Error Detection and Formal Methods

During post-silicon validation, manufactured integrated circuits are extensively tested in actual system environments to detect design bugs. Bug localization involves identification of a bug trace (a sequence of inputs that activates and detects the bug) and a hardware design block where the bug is located. Existing bug localization practices during post-silicon validation are mostly manual and ad hoc, and, hence, extremely expensive and time consuming. This is particularly true for subtle electrical bugs caused by unexpected interactions between a design and its electrical state. We present E-QED, a new approach that automatically localizes electrical bugs during post-silicon validation. Our results on the OpenSPARC T2, an open-source 500-million-transistor multicore chip design, demonstrate the effectiveness and practicality of E-QED: starting with a failed post-silicon test, in a few hours (9 hours on average) we can automatically narrow the location of the bug to (the fan-in logic cone of) a handful of candidate flip-flops (18 flip-flops on average for a design with ~ 1 Million flip-flops) and also obtain the corresponding bug trace. The area impact of E-QED is ~2.5%. In contrast, deter-mining this same information might take weeks (or even months) of mostly manual work using traditional approaches

An Attribute Selection For Severity Level Determination According To The Support Vector Machine Classification Result

Determination of bug severity level is needed in fixing bug. Actually, in bug-tracking system, there is around 14 attributes used for defining a bug. But, all this time we do not know which attributes are highly influential for this. In this research, a new model of severity type classification using Infogain method for Bugzilla is proposed. As for the classsification process, we use Support Vector Machine, because this method is suitable in handling a massive data records. In this research, 8 bug attributes and 17.746 record of bug reports are involved. From the result of the experiment, we recommend five attributes which can be used effectively in classifying the severity types with a minimal value of infogain 0,33 which is component, qa_contact, summary, cc_list and product. The combination of those 5 attributes resulting in 99,83% accuracy of severity types classification. Keywords- Bug Tracking System; Severity Level Classification; TF-IDF; Infogain; SVM

Usability discussions in open source development

The public nature of discussion in open source projects provides a valuable resource for understanding the mechanisms of open source software development. In this paper we explore how open source projects address issues of usability. We examine bug reports of several projects to characterise how developers address and resolve issues concerning user interfaces and interaction design. We discuss how bug reporting and discussion systems can be improved to better support bug reporters and open source developers