Spectrometric Identification of Organic Compounds

LNBIP reports state-of-the-art results in areas related to business information systems and industrial application software development – timely, at a high level, and in both printed and electronic form.  The type of material published includes  • Proceedings (published in time for the respective event) • Postproceedings (consisting of thoroughly revised and/or extended final papers) • Other edited monographs (such as, for example, project reports or invited volumes) • Tutorials (coherently integrated collections of lectures given at advanced courses, seminars, schools, etc.) • Award-winning or exceptional theses LNBIP is abstracted/indexed in DBLP, EI and Scopus. LNBIP volumes are also submitted for the inclusion in ISI Proceedings.

Lecture notes in business information processing

During the recent years, the market of mobile software applications (apps) has maintained an impressive upward trajectory. Many small and large software development companies invest considerable resources to target available opportunities. As of today, the markets for such devices feature over 850K+ apps for Android and 900K+ for iOS. Availability, cost, functionality, and usability are just some factors that determine the success or lack of success for a given app. Among the other factors, reliability is an important criteria: users easily get frustrated by repeated failures, crashes, and other bugs; hence, abandoning some apps in favor of others. This paper reports a study analyzing how the fault- and change-proneness of APIs used by 7,097 (free) Android apps relates to applications' lack of success, estimated from user ratings. Results of this study provide important insights into a crucial issue: making heavy use of fault- and change-prone APIs can negatively impact the success of these apps.

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API change and fault proneness: a threat to the success of Android apps

Static analysis tools help developers find bugs, improve code readability, and ensure consistent style across a project. However, these tools can be difficult to smoothly integrate with each other and into the developer workflow, particularly when scaling to large codebases. We present Tricorder, a program analysis platform aimed at building a data-driven ecosystem around program analysis. We present a set of guiding principles for our program analysis tools and a scalable architecture for an analysis platform implementing these principles. We include an empirical, in-situ evaluation of the tool as it is used by developers across Google that shows the usefulness and impact of the platform.

/pdf/tricorder-building-a-program-analysis-ecosystem-4s869daajb.pdf

Tricorder: building a program analysis ecosystem

Process mining is a growing and promising study area focused on understanding processes and to help capture the more significant findings during real execution rather than, those methods that, only observed idealized process model. The objective of this article is to map the active research topics of process mining and their main publishers by country, periodicals, and conferences. We also extract the reported application studies and classify these by exploration domains or industry segments that are taking advantage of this technique. The applied research method was systematic mapping, which began with 3713 articles. After applying the exclusion criteria, 1278 articles were selected for review. In this article, an overview regarding process mining is presented, the main research topics are identified, followed by identification of the most applied process mining algorithms, and finally application domains among different business segments are reported on. It is possible to observe that the most active research topics are associated with the process discovery algorithms, followed by conformance checking, and architecture and tools improvements. In application domains, the segments with major case studies are healthcare followed by information and communication technology, manufacturing, education, finance, and logistics.

Process mining techniques and applications – A systematic mapping study

While speculatively executing a given one of a plurality of transactions concurrently executing on a computer, carry out write operations in a local data block, and automatically create an entry in a checkpoint log when reading from a shared memory. Continuously conflict check during read and tentative commit operations, and carry out a partial rollback upon detection of a conflict. The partial rollback is based upon the checkpoint log.

Automatic checkpointing and partial rollback in software transaction memory

Issue reporting and resolution is a software engineering process supported by tools such as Issue Tracking System (ITS), Peer Code Review (PCR) system and Version Control System (VCS). Several open source software projects such as Google Chromium and Android follow process in which a defect or feature enhancement request is reported to an issue tracker followed by source-code change or patch review and patch commit using a version control system. We present an application of process mining three software repositories (ITS, PCR and VCS) from control flow and organizational perspective for effective process management. ITS, PCR and VCS are not explicitly linked so we implement regular expression based heuristics to integrate data from three repositories for Google Chromium project. We define activities such as bug reporting, bug fixing, bug verification, patch submission, patch review, and source code commit and create an event log of the bug resolution process. The extracted event log contains audit trail data such as caseID, timestamp, activity name and performer. We discover runtime process model for bug resolution process spanning three repositories using process mining tool, Disco, and conduct process performance and efficiency analysis. We identify bottlenecks, define and detect basic and composite anti-patterns. In addition to control flow analysis, we mine event log to perform organizational analysis and discover metrics such as handover of work, subcontracting, joint cases and joint activities.

Process mining multiple repositories for software defect resolution from control and organizational perspective

Issue tracking systems such as Bugzilla, Mantis and JIRA are Process Aware Information Systems to support business process of issue (defect and feature enhancement) reporting and resolution. The process of issue reporting to resolution consists of several steps or activities performed by various roles (bug reporter, bug triager, bug fixer, developers, and quality assurance manager) within the software maintenance team. Project teams define a workflow or a business process (design time process model and guidelines) to streamline and structure the issue management activities. However, the runtime process (reality) may not conform to the design time model and can have imperfections or inefficiencies. We apply business process mining tools and techniques to analyze the event log data (bug report history) generated by an issue tracking system with the objective of discovering runtime process maps, inefficiencies and inconsistencies. We conduct a case-study on data extracted from Bugzilla issue tracking system of the popular open-source Firefox browser project. We present and implement a process mining framework, Nirikshan, consisting of various steps: data extraction, data transformation, process discovery, performance analysis and conformance checking. We conduct a series of process mining experiments to study self-loops, back-and-forth, issue reopen, unique traces, event frequency, activity frequency, bottlenecks and present an algorithm and metrics to compute the degree of conformance between the design time and the runtime process.

Nirikshan: mining bug report history for discovering process maps, inefficiencies and inconsistencies

Given the high costs of software testing and fixing bugs after release, early detection of bugs using static analysis can result in significant savings. However, despite their many benefits, recent availability of many such tools, and evidence of a positive return-on-investment, static-analysis tools are not used widely because of various usability and usefulness problems. The usability inhibitors include the lack of features, such as capabilities to merge reports from multiple tools and view warning deltas between two builds of a system. The usefulness problems are related primarily to the accuracy of the tools: identification of false positives (or, spurious bugs) and uninteresting bugs among the true positives. In this paper, we present the details of an online portal, developed at IBM Research, to address these problems and promote the adoption of static-analysis tools. We report our experience with the deployment of the portal within the IBM developer community. We also highlight the problems that we have learned are important to address, and present our approach toward solving some of those problems.

Making defect-finding tools work for you

Techniques for static code analysis are provided. The techniques include routing a code analysis request to a set of one or more static code analysis tools based on a context of the request, merging one or more tool-specific outputs of the code analysis, wherein merging the one or more tool-specific outputs comprises using one or more tool-specific mappings to one or more static code analysis abstractions and one or more source linkages of one or more static code analysis defects, performing a deep analysis on a set of one or more defect categories, and combining results of the merging with the deep analysis to filter out one or more false-positives and to add one or more new true positives to create an optimal set of defects.

Monika Gupta

Papers

Automatic checkpointing and partial rollback in software transaction memory

Process mining multiple repositories for software defect resolution from control and organizational perspective

Nirikshan: mining bug report history for discovering process maps, inefficiencies and inconsistencies

Making defect-finding tools work for you

Static code analysis