Sangeeta Lal

Comparison of Seven Bug Report Types: A Case-Study of Google Chrome Browser Project

Bug reports submitted to an issue tracking system can belong to different categories such as crash, regression, security, cleanup, polish, performance and usability. A deeper understanding of the properties and features of various categories of bug reports can have implications in improving software maintenance processes, tools and practices. We identify several metrics and characteristics serving as dimensions on which various types of bug reports can be compared. We perform a case-study on Google Chromium Browser open-source project and conduct a series of experiments to calculate various metrics. We present a characterization study comparing different types of bug reports on metrics such as: statistics on close-time, number of stars, number of comments, discriminatory and frequent words for each class, entropy across reporters, entropy across component, opening and closing trend, continuity and debugging efficiency performance characteristics. The calculated metrics shows the similarities and differences on various dimensions for seven different types of bug reports.

Improving Logging Prediction on Imbalanced Datasets: A Case Study on Open Source Java Projects

Logging is an important yet tough decision for OSS developers. Machine-learning models are useful in improving several steps of OSS development, including logging. Several recent studies propose machine-learning models to predict logged code construct. The prediction performances of these models are limited due to the class-imbalance problem since the number of logged code constructs is small as compared to non-logged code constructs. No previous study analyzes the class-imbalance problem for logged code construct prediction. The authors first analyze the performances of J48, RF, and SVM classifiers for catch-blocks and if-blocks logged code constructs prediction on imbalanced datasets. Second, the authors propose LogIm, an ensemble and threshold-based machine-learning model. Third, the authors evaluate the performance of LogIm on three open-source projects. On average, LogIm model improves the performance of baseline classifiers, J48, RF, and SVM, by 7.38%, 9.24%, and 4.6% for catch-blocks, and 12.11%, 14.95%, and 19.13% for if-blocks logging prediction.

Two Level Empirical Study of Logging Statements in Open Source Java Projects

Log statements present in source code provide important information to the software developers because they are useful in various software development activities. Most of the previous studies on logging analysis and prediction provide insights and results after analyzing only a few code constructs. In this paper, the authors perform an in-depth and large-scale analysis of logging code constructs at two levels. They answer nine research questions related to statistical and content analysis. Statistical analysis at file level reveals that fewer files consist of log statements but logged files have a greater complexity than that of non-logged files. Results show that a positive correlation exists between size and logging count of the logged files. Statistical analysis on catch-blocks show that try-blocks associated with logged catch-blocks have greater complexity than non-logged catch-blocks and the logging ratio of an exception type is project specific. Content-based analysis of catch-blocks reveals the presence of different topics in try-blocks associated with logged and non-logged catch-blocks.

Effective asthma disease prediction using naive Bayes — Neural network fusion technique

Asthma is a lung disease caused by the inflammation and narrowing of the airways that causes recurrent attacks of breathlessness and wheezing, and often can be life-threatening. Around 15-20 million people are suffering from asthma in India[1]. This paper aims at analyzing various data mining techniques for the prediction of asthma. The observations show that the fusion approach of naive bayes and neural network proved to be the best among classification algorithms in the diagnosis of asthma. This methodology is evaluated using 1024 raw data obtained from a city hospital. The proposed approach helps patients in their diagnosis of asthma.

Samekana: A Browser Extension for Including Relevant Web Links in Issue Tracking System Discussion Forum

Several widely used Issue tracking systems (such as Google Issue Tracker and Bugzilla) contains an integrated threaded discussion forum to facilitate discussion between the development and maintenance team (bug reporters, bug triagers, bug fixers and quality assurance managers). We observe that several comments (and even bug report descriptions) posted to issue tracing system contains links to external websites as references to knowledge sources relevant to the discussion. We conduct a survey (and present the results of the survey) of Google Chromium Developers on the importance and usefulness of web references in issue tracking system comments and the need of a web-browser extension which facilitates easy organization and inclusion of web-links in the post. We conduct a characterization study on an experimental dataset from Google Chromium Issue Tracking system and present results on the distribution of number of links in the dataset, categorization of links into pre-defined classes (such as blogs, community based Q&A websites, developer discussion forums, version control system), correlation of number and types of links with various bug report types (such as security, crash, regression and clean-up) and relation between presence of links and bug resolution time. Survey results and data characterization study motivate the need of building a developer productivity tool to facilitate web-link (as references) organization and inclusion in issue tracking system comments. We present a Google Chromium Web Browser Extension called as Samekana and publish the extension on Google Chromium Web Store which can be freely downloaded by users worldwide. The extension contains features such as annotating (using tags, title and description) and saving web references pertaining to multiple bug reports and tasks and then posting it as bibliography (for easy citation and reference) in issue tracking system comments.

Logger4u: Predicting debugging statements in the source code

Software logging is an essential programming practice that saves important runtime information that can be used later by software developers for troubleshooting, debugging and monitoring the software. Even though software logging has numerous benefits this practice is underutilized because of lack of any formal guiding principles to developers for making strategic and efficient logging decisions. Logging should be optimized because too much logging can cause performance overheads; sparse logging can leave out vital information that might give clues to developers about the real issues. In absence of any formal guidelines developers rely solely on their domain knowledge and experience while making logging decisions. In order to lessen this effort of making decisions we have proposed a machine learning based framework, Logger4u for if-block logging prediction. We extract and use 28 distinctive static features from the source code helpful in making well informed logging decisions. We use Support Vector Machine (two variants, 1 linear and 1 RBF kernel based) models, Multilayer Perceptron with back propagation model and Random forest model in our work. Our approach gives encouraging results for if-block logging task. The accuracy achieved by the Linear SVM, MLP, Random Forest and kernel SVM are 73.05%, 74.62%, 79.84% and 81.22% respectively

Migrated Question Prediction on StackExchange

Stack Exchange (SE) is a network of popular Community based Question and Answering (CQA) websites. Each SE Q&A website is created to address questions on specific user interest or domain. However, often users post questions on SE websites that do not match the domain of the website. Such questions are considered as Off-topic for the origin site. Off-topic questions must be detected and migrated to more appropriate On-Topic site in SE network. Off-topic questions are migrated manually to other sites by moderators or experts users (through a voting process). The process of migrating questions from one site to other is known as Question Migration. We study migrated questions on SE Q&A website. We identify several distinguishing features of migrated questions and propose a machine learning based framework to predict migrating questions. Effectiveness of proposed model is tested on five SE Q&A sites. Experimental results demonstrate that the proposed model is effective (maximum accuracy of 73%) in predicting migrating questions.