Web Services are application components characterised by interoperability, extensibility, distributed application development and service oriented architecture. A complex distributed application can be developed by combing several third-party web-services. Anti-patterns are counter-productive and poor design and practices. Web-services suffer from a multitude of anti-patterns such as God object Web service and Fine grained Web service. Our work is motivated by the need to build techniques for automatically detecting common web-services anti-patterns by static analysis of the source code implementing a web-service. Our approach is based on the premise that summary values of object oriented source code metrics computed at a web-service level can be used as a predictor for anti-patterns. We present an empirical analysis of 4 data sampling techniques to encounter the class imbalance problem, 5 feature ranking techniques to identify the most informative and relevant features and 8 machine learning algorithms for predicting 5 different types of anti-patterns on 226 real-world web-services across several domains. We conclude that it is possible to predict anti-patterns using source code metrics and a machine learning framework. Our analysis reveals that the best performing classification algorithm is Random Forest, best performing data sampling technique is SMOTE and the best performing feature ranking method is OneR.

Source code refactoring consisting of modifying the structure of the source code without changing its functionality and external behavior. We present a method to predict refactoring candidates at class level which can help developers in improving their design and structure of source code while preserving the behavior. We propose a technique to predict refactoring candidates based on the application of a machine learning based framework. We use Least Squares Support Vector Machines (LS-SVM) as the learning algorithm, Principal Component Analysis (PCA) as a feature extraction technique and Synthetic Minority Over-sampling Technique (SMOTE) as a technique for handling imbalanced data. We start with 102 source code metrics as input features which are then reduced to 31 features after removing irrelevant and redundant features through statistical tests. We conduct a series of experiments on publicly available software engineering dataset consisting of seven open-source software systems in which the refactored classes are manually validated. We apply LS-SVM with three different functions: linear, polynomial and Radial Basis Function (RBF). Statistical significance test demonstrate that RBF kernel outperforms linear and polynomial kernel but there is no statistically significant difference between the performance of linear and polynomial kernel. Statistical significance test reveals that with-SMOTE technique outperforms without-SMOTE and all metrics outperforms PCA based metrics. The mean value of Area Under Curve (AUC) for LS-SVM RBF kernel is 0.96.

Software fault prediction models are employed to optimize testing resource allocation by identifying fault-prone classes before testing phases. We apply three different ensemble methods to develop a model for predicting fault proneness. We propose a framework to validate the source code metrics and select the right set of metrics with the objective to improve the performance of the fault prediction model. The fault prediction models are then validated using a cost evaluation framework. We conduct a series of experiments on 45 open source project dataset. Key conclusions from our experiments are: (1) Majority Voting Ensemble (MVE) methods outperformed other methods (2) selected set of source code metrics using the suggested source code metrics using validation framework as the input achieves better results compared to all other metrics (3) fault prediction method is effective for software projects with a percentage of faulty classes lower than the threshold value (low - 54.82%, medium - 41.04%, high - 28.10%).

Case-Based Learning (CBL) is a teaching methodology based on discussing and analyzing real world problems and solutions. A case is like a story, related to a real world situation that sources a number of challenging problems, which have no obvious solutions. There have been various applications of CBL in the fields of Medicine, Law, and Business. However, there are a limited number of evidences related to the application of CBL in the field of Software Engineering (SE). In this paper, we present an open source web application called SEABED (Software Engineering Case-Based Learning Database). The feature set supported by SEABED comprises Case Submission, Case Collection, Case Search, Case Review, and Case Evolution. SEABED aims to develop and evolve a rich repository of SE cases that might become a basis for enabling the students, instructors, practitioners, and experts to enhance their SE knowledge in an effective way. Further, we present our approach to build a vibrant SE case-based learning community that triggers enough activity around SEABED, required for the platform to reach a critical and wider mass. We communicated with several SE educators around the world and received positive responses on SEABED. In order to investigate the effectiveness of the CBL methodology followed by SEABED, we conducted an experimental study at an Institute of National Importance in India. We present the empirical analysis results of this study and explore the impact of CBL on students' learning abilities.

We conduct a bibliometric and scientific publication mining based study to understand how the APSEC conference has evolved over the recent past 6 years (year 2010 to 2015). Our objective is to perform an in-depth examination of the state of APSEC so that the APSEC community can identify strengths, areas of improvements and future directions for the conference. Our empirical analysis is based on various perspectives such as: paper submission acceptance rate trends, scholarly productivity and contributions from various countries, identification of prolific authors, computation of citation impact of papers and contributing authors, internal and external collaboration, university and industry participation and collaboration, measurement of gender imbalance, yearly author churn and program committee characteristics.

Issue Tracking Systems (ITS) such as Bugzilla can be viewed as Process Aware Information Systems (PAIS) generating event-logs during the life-cycle of a bug report. Process Mining consists of mining event logs generated from PAIS for process model discovery, conformance and enhancement. We apply process map discovery techniques to mine event trace data generated from ITS of open source Firefox browser project to generate and study process models. Bug life-cycle consists of diversity and variance. Therefore, the process models generated from the event-logs are spaghetti-like with large number of edges, inter-connections and nodes. Such models are complex to analyse and difficult to comprehend by a process analyst. We improve the Goodness (fitness and structural complexity) of the process models by splitting the event-log into homogeneous subsets by clustering structurally similar traces. We adapt the K-Medoid clustering algorithm with two different distance metrics: Longest Common Subsequence (LCS) and Dynamic Time Warping (DTW). We evaluate the goodness of the process models generated from the clusters using complexity and fitness metrics. We study back-forth and self-loops, bug reopening, and bottleneck in the clusters obtained and show that clustering enables better analysis. We also propose an algorithm to automate the clustering process-the algorithm takes as input the event log and returns the best cluster set.

Software logging is a common software development practice which is used to log program execution points. This execution information is later used by software developers for debugging purpose. Software logging is useful but it has cost and benefit tradeoff. Hence it is important to optimize the number of log statements in the code. However, previous studies on logging show that optimal logging is challenging for software developers. Hence tools and techniques which can help developers in making optimized logging decision can be beneficial. We propose LogOptPlus, a machine learning based tool to help software developers to optimize the number of log statements in the source code, for two focused code construct types. LogOptPlus is a significant extension of our previously published work 'LogOpt'. LogOpt is designed to predict logging for catch-blocks. We extend the functionality of LogOpt to predict logging for both if-blocks and catch-blocks. We identify distinguishing static features from the source code for logging prediction. We present intuition and results of quantitative analysis of all the features. We present results of comprehensive evaluation of LogOptPlus with five different machine learning algorithms on two two large Open Source projects (i.e., Apache Tomcat and CloudStack). Encouraging experimental results on two Open Source projects show that LogOptPlus is effective in logging prediction for two focused code construct types.

Stack Overflow is a popular community-based Q&A website that caters to technical needs of software developers. As of February 2015 -- Stack Overflow has more than 3.9M registered users, 8.8M questions, and 41M comments. Stack Overflow provides explicit and detailed guidelines on how to post questions but, some questions are very poor in quality. Such questions are deleted by the experienced community members and moderators. Deleted questions increase maintenance cost and have an adverse impact on the user experience. Therefore, predicting deleted questions is an important task. In this study, we propose a two stage hybrid approach -- DelPredictor -- which combines text processing and classification techniques to predict deleted questions. In the first stage, DelPredictor converts text in the title, body, and tag fields of questions into numerical textual features via text processing and classification techniques. In the second stage, it extracts meta features that can be categorized into: profile, community, content, and syntactic features. Next, it learns and combines two independent classifiers built on the textual and meta features. We evaluate DelPredictor on 5 years (2008 -- 2013) of deleted questions from Stack Overflow. Our experimental results show that DelPredictor improves the F1-scores over baseline prediction, a prior approach [12] and a text-based approach by 29.50%, 9.34%, and 28.11%, respectively.

Internet of Things (IoT) application development is technically challenging and complex due to several factors such as the presence of different IoT protocols, interoperability issues, lack of industry standards and dealing with the heterogeneity that exists both in Physical and Internet worlds. Furthermore, developers involved in the IoT application development have to address issues pertaining to different life-cycles ranging from design, implementation to deployment (deployment complexities due to the possibilities of wide range of devices). Manual effort involved in all above three phases for heterogeneous devices is a time-consuming and error-prone process. We demonstrate IoTSuite which is an application development platform consisting of a suite of tools motivated by the need to simplify IoT application development. The IoTSuite supports several features such as automatic code generation and integration with a set of modeling languages. It takes high-level specification as input that abstract heterogeneity related complexity. It integrates compiler and deployment module to provide automation at different phases of application development process.

The configuration of a system determines the system behavior and wrong configuration settings can adversely impact system's availability, performance, and correctness. We refer to these wrong configuration settings as configuration bugs. The importance of configuration bugs has prompted many researchers to study it, and past studies can be grouped into three categories: detection, localization, and fixing of configuration bugs. In the work, we focus on the detection of configuration bugs, in particular, we follow the line-of-work that tries to predict if a bug report is caused by a wrong configuration setting. Automatically prediction of whether a bug is a configuration bug can help developers reduce debugging effort. We propose a novel approach named EFSPredictor which applies ensemble feature selection on the natural-language description of a bug report. It uses different feature selection approaches (e.g., ChiSquare, GainRatio and Relief) which output different ranked lists of textual features. Next, to obtain a set of representative textual features, EFSPredictor first assigns different scores to the features outputted by these feature selection approaches. Next, for each feature, EFSPredictor sums up the scores outputted by the multiple ranked lists, and outputs the top features (e.g., 25% of the total number of features) as the selected features. Finally, EFSPredictor builds a prediction model based on the selected features. We conduct experiments on 5 bug report datasets (i.e., accumulo, activemq, camel, flume, and wicket) containing a total of 3,203 bugs. The experiment results show that, on average across the 5 projects, EFSPredictor achieves an F1-score to 0.57, which improves the state-of-the-art approach proposed by Xia et al. by 14%.

Peer code review and inspection is a quality improvementsoftware engineering activity consisting of systematicexamination of source code. While peer code review is commonlyused in industrial and open-source software projects, it is seldomtaught or practiced in undergraduate level Software Engineeringcourses. We conduct a study on the use of peer code reviewin a sophomore level introductory Software Engineering courseconsisting of more than 200 students and present our experiences,findings and challenges. We use Bitbucket's (a free code distributedversion control system hosting site for Git and Mercurial)in-built code-review system and web-based hosting service. Weextract the peer code review comments using Bitbucket API fordetecting coding standard or compliance violation and identificationof defects (number and type) by reviewers. We also conducta survey on the benefit of peer code review activity on peercohesion and communication. Our experiments and survey revealthat employing peer code review in an undergraduate class hasseveral learning benefits such as improvement in coding skills,program comprehension abilities, knowledge of coding standard,and compliance and peer communication

Process-Aware Information System (PAIS) are IT systems that manages, supports business processes and generate large event logs from execution of business processes. Process Mining consists of analyzing event logs generated by PAISs and discover business process models and check for conformance between the discovered and actual models. The large volume of event logs generated are stored in databases. Relational databases perform well for certain class of applications. However, there are certain class of applications for which relational databases are not able to scale. Several NoSQL databases have emerged to encounter the challenges of scalability in traditional databases. Discovering social network from event logs is one of the most challenging and important Process Mining task. Similar-Task algorithm is one of the most widely used Organizational Mining techniques. Our objective is to investigate which of the databases (Relational or Graph) perform better for Organizational Mining under Process Mining. We implement Similar-Task algorithm on relational and NoSQL (graph oriented) databases using only query language constructs. We conduct empirical analysis on a large real world data set to compare the performance of row-oriented database and NoSQL graph-oriented database.

Process mining consists of mining event logs generated from business process execution supported by Information Systems (IS). Process mining of software repositories has diverse applications because vast data is generated during Software Development Life Cycle (SDLC) and archived in IS such as Version Control System (VCS), Peer Code Review (PCR) System, Issue Tracking System (ITS), and mail archives. There is need to explore its applications on different repositories to aid managers in process management. We conduct two phase surveys and interviews with managers in a large, global, IT company. The first survey and in-person interviews identify the process challenges encountered by them that can be addressed by novel applications of process mining. We filter, group and abstract responses formulating 30 generic problem statements. On the basis of process mining type, we classify identified problems to eight categories such as control analysis, organizational analysis, conformance analysis, and preventive analysis. The second survey asks distinct participants the importance of solving identified problems. We calculate proposed Net Importance Metric (NIM) using 1262 ratings from 43 participants. Combined analysis of NIM and first survey responses reveals that the problems mentioned by few practitioners in first survey are considered important by majority in the second survey. We elaborate on possible solutions and challenges for most frequent and important problems. We believe solving these validated problems will help managers in improving project quality and productivity.

Online Analytical Processing (OLAP) cube is a multi-dimensional dataset used for analyzing data in a Data Warehouse (DW) for the purpose of extracting actionable intelligence. Process mining consists of analyzing event log data produced from Process Aware Information Systems (PAIS) for the purpose of discovering and improving business processes. Process cube is a concept which falls at the intersection of OLAP cube and process mining. Process cube facilitates process mining from multiple-dimensions and enables comparison of process mining results across various dimensions. We present an application of process cube to software defect resolution process to analyze and compare process data from a multi-dimensional perspective. We present a framework, a novel perspective to mine software repositories using process cube. Each cell of process cube is defined by metrics from multiple process mining perspectives like control flow, time, conformance and organizational perspective. We conduct a case-study on Google Chromium project data in which the software defect resolution process spans three software repositories: Issue Tracking System (ITS), Peer Code Review System (PCR) and Version Control System (VCS). We define process cube with 9 dimensions as issue report timestamp, priority, state, closed status, OS, component, bug type, reporter and owner. We define hierarchies along various dimensions and cluster members to handle sparsity. We apply OLAP cube operations such as slice, dice, roll-up and drill-down, and create materialized sub log for each cell. We demonstrate the solution approach by discovering process map and compare process mining results from Control Flow and Time perspective for Performance and Security issues.

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.

Free/Libre Open Source Software (FLOSS) community management is an important issue. Contributor churn (joining or leaving a project) causes failure of the majority of software projects. In this paper, we present a framework to characterize stability of the community in software maintenance projects by mining Issue Tracking System (ITS). We identify key stability indicators and propose metrics to measure them. We conduct time series analysis on metrics data to examine the stability of the community. We model community participation patterns and forecast future behavior to help plan and support informed decision making. We present a case study of four years data of Google Chromium Project and investigate the inferential ability of the framework.

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.

YouTube is one of the most popular and largest video sharing websites (with social networking features) on the Internet. A significant percentage of videos uploaded on YouTube contains objectionable content and violates YouTube community guidelines. YouTube contains several copyright violated videos, commercial spam, hate and extremism promoting videos, vulgar and pornographic material and privacy invading content. This is primarily due to the low publication barrier and anonymity. We present an approach to identify privacy invading harassment and misdemeanor videos by mining the video metadata. We divide the problem into sub-problems: vulgar video detection, abuse and violence in public places and ragging video detection in school and colleges. We conduct a characterization study on a training dataset by downloading several videos using YouTube API and manually annotating the dataset. We define several discriminatory features for recognizing the target class objects. We employ a one class classifier approach to detect the objectionable video and frame the problem as a recognition problem. Our empirical analysis on test dataset reveals that linguistic features (presence of certain terms and people in the title and description of the main and related videos), popularity based, duration and category of videos can be used to predict the video type. We validate our hypothesis by conducting a series of experiments on evaluation dataset acquired from YouTube. Empirical results reveal that accuracy of proposed approach is more than 80% demonstrating the effectiveness of the approach.

Stack Overflow is the most popular Community based Question Answering (CQA) website for programmers on the web with 2.05M users, 5.1M questions and 9.4M answers. Stack Overflow has explicit, detailed guidelines on how to post questions and an ebullient moderation community. Despite these precise communications and safeguards, questions posted on Stack Overflow can be extremely off topic or very poor in quality. Such questions can be deleted from Stack Overflow at the discretion of experienced community members and moderators. We present the first study of deleted questions on Stack Overflow. We divide our study into two parts - (i) Characterization of deleted questions over ~5 years (2008-2013) of data, (ii) Prediction of deletion at the time of question creation. Our characterization study reveals multiple insights on question deletion phenomena. We find that it takes substantial time to vote a question to be deleted but once voted, the community takes swift action. We also see that question authors delete their questions to salvage reputation points. We notice some instances of accidental deletion of good quality questions but such questions are voted back to be undeleted quickly. We discover a pyramidal structure of question quality on Stack Overflow and find that deleted questions lie at the bottom (lowest quality) of the pyramid. We also build a predictive model to detect the deletion of question at the creation time. We experiment with 47 features -- based on User Profile, Community Generated, Question Content and Syntactic style -- and report an accuracy of 66%. Our findings reveal important suggestions for content quality maintenance on community based question answering websites. To the best of our knowledge, this is the first large scale study on poor quality (deleted) questions on Stack Overflow.

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.

Contribution and performance assessment is an established practice in organization with its sphere of influence spanning process, policy, people (client) and personnel (employee). Multiple roles in organization (for e.g. employees, project manager, hr manager etc.) view contribution and performance assessment with different perspectives and objectives. However, despite its ability to ensure growth and affluence of organization existing contribution and performance assessment practices are marred with flaws and imperfections. The objective of this work is to serve wide-ranged requirements of software maintenance professionals (in context of contribution and performance assessment) in organization by application of variegated approaches (tools, techniques, models etc.) on software repositories (Issue Tracking System, Version Control System, Source Code Repositories etc.). We study pain-points of practitioners (in context of contribution and performance assessment) and present solution approach by mining software repositories. We implement the approach on real-world data and gather insights from practitioners to validate and improve our research. In this paper, 'SamikshaUmbra' (research umbrella that circumscribe related research threads) we present approach to meet stated research objectives and viable research directions.

YouTube is one of the largest video sharing websites (with social networking features) on the Internet. The immense popularity of YouTube, anonymity and low publication barrier has resulted in several forms of misuse and video pollution such as uploading of malicious, copyright violated and spam video or content. YouTube has a popular and commonly used feature called as video response which allows users to post a video response to an uploaded or existing video. Some of the popular videos on YouTube receive thousands of video responses. We observe presence of opportunistic users posting unrelated, promotional, pornographic videos (spam videos posted manually or using automated scripts) as video responses to existing videos. We present a method of mining YouTube to automatically detect video response spam. We formulate the problem of video response spam detection as a one-class classification problem (a recognition task) and divide the problem into three sub-problems: promotional video recognition, pornographic or dirty video recognition and automated script or botnet uploader recognition. We create a sample dataset of target class videos for each of the three sub-problems and identify contextual features (meta-data based or non-content based features) characterizing the target class. Our empirical analysis reveals that certain linguistic features (presence of certain terms in the title or description of the YouTube video), temporal features, popularity based features, time based features can be used to predict the video type. We identify features with discriminatory powers and use it within a one-class classification framework to recognize video response spam. We conduct a series of experiments to validate the proposed approach and present evidences to demonstrate the effectiveness of the proposed solution with more than 80% accuracy.

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.

Twitter is a popular micro-blogging website which allows users to post 140-character limit messages called tweets. We demonstrate a cheap and elegant solution - WhACKY! - to harness the multi-source information from tweets to link Twitter profiles across other external services. In particular, we exploit activity feed sharing patterns to map Twitter profiles to their corresponding external service accounts using publicly available APIs. We illustrate a proof-of-concept by mapping 69,496 Twitter profiles to at least one of the five popular external services : Flickr (photo-sharing service), Foursquare (location-based service), YouTube (video-sharing service), Facebook (a popular social network) and LastFM (music-sharing service). We evaluate our solution against a commercial social identity mapping service - FlipTop - and demonstrate the efficiency of our approach. WhACKY! guarantees that the mapped profiles are 100% true-positive and helps quantify the unintended leakage of Personally Identifiable Information (PII) attributes. During the process, WhACKY! is also able to detect duplicate Twitter profiles connected to multiple external services.We also develop a web application based on WhACKY!1 for perusal by Twitterers which can help them better understand unintended leakage of their PII.

Defect tracking systems such as Bugzilla and JIRA and source code version control systems such as CVS and SVN are widely used applications to support software development and maintenance activities. Previous studies show that bug databases and version databases are often used as standalone and separate repositories without explicit linkages between issue reports and corresponding commit transactions. This is because developers often do not explicitly mention or tag commit transactions with the relevant bug report IDs. The lack of explicit links between these two databases has been identified as a serious process data quality issue (incomplete and biased data) having implications in predictive model building (such as defect density and error proneness computation) and hypothesis-testing based on the dataset. Researchers have proposed solutions to link the two databases and performed experiments on open source projects such as Fire Fox Mozilla. We review previous approaches and propose a novel technique (based on Fellegi-Sunter (FS) Model for record linkages) to automatically integrate the two databases that overcomes some of the drawbacks of traditional methods. We validate the proposed approach by performing experiments on publicly available bug and version dataset obtained from two open-source projects (Apache HTTP Server and WikiMedia). The results of our experiments demonstrate that the proposed solution is effective in recovering trace ability links (missing links) between bug-fixing commits and corresponding bug reports.

We present an approach to identify duplicate bug reports expressed in free-form text. Duplicate reports needs to be identified to avoid a situation where duplicate reports get assigned to multiple developers. Also, duplicate reports can contain complementary information which can be useful for bug fixing. Automatic identification of duplicate reports (from thousands of existing reports in a bug repository) can increase the productivity of a Triager by reducing the amount of time a Triager spends in searching for duplicate bug reports of any incoming report. The proposed method uses character N-gram-based model for the task of duplicate bug report detection. Previous approaches are word-based whereas this study investigates the usefulness of low-level features based on characters which have certain inherent advantages (such as natural-language independence, robustness towards noisy data and effective handling of domain specific term variations) over word-based features for the problem of duplicate bug report detection. The proposed solution is evaluated on a publicly-available dataset consisting of more than 200 thousand bug reports from the open-source Eclipse project. The dataset consists of ground-truth (pre-annotated dataset having bug reports tagged as duplicate by the Triager). Empirical results and evaluation metrics quantifying retrieval performance indicate that the approach is effective.

Studying link structure of the World Wide Web (WWW) is an area which has attracted a lot of interest. Several papers have been published on structural analysis of hyperlinked environments such as the WWW. The WWW can be modeled as a graph and valuable information can be derived by analyzing links between the Web-pages primarily for the purpose of building better search engines. Many novel methods have been presented to discover communities from the WWW and discover authoritative Web-pages. Citation analysis is a branch of information science on which plenty of research has been done. Citation analysis pertains to analysis of articles and research paper citations in a scholarly field and deriving useful information from it. It has primarily been used as a useful tool to quantify and judge the impact of a paper or a journal. The work presented in this paper lies at the intersection of the two fields: structural analysis of WWW and citation analysis. In this paper, we present a method for classifying documents (such as articles and patents containing references) to a class or topic based on their link structure, references and citations. The method consists of analyzing the link structure of a corpus to first identify authoritative papers and assigning a class label to them. The class labels are assigned manually by a domain expert by going through the respective documents. The next step consists of identifying related papers to the authoritative papers using citation analysis. The authoritative papers, their class labels and their related papers constitute a model. Papers for which class label needs to be determined are classified based on the created model.

Many non-cooperative settings that could potentially be studied using game theory are characterized by having very large strategy spaces and payoffs that are costly to compute. Best response dynamics is a method of searching for pure-strategy equilibria in games that is attractive for its simplicity and scalability (relative to more analytical approaches). However, when the cost of determining the outcome of a particular set of joint strategies is high, it is impractical to compute the payoffs of all possible responses to the other players actions. Thus, we study metaheuristic approaches--genetic algorithms and tabu search in particular--to explore the strategy space. We configure the parameters of metaheuristics to adapt to the problem of finding the best response strategy and present how it can be helpful in finding Nash equilibria of combinatorial auctions which is an important solution concept in game theory.

Proxy bidding has proved useful in a variety of real auction formats, such as eBay, and has been proposed for some combinatorial auctions. Previous work on proxy bidding in combinatorial auctions requires the auctioneer essentially run the auction with myopic bidders to determine the outcome. In addition to being computationally costly, this process is only as accurate as the bid increment, and decreasing the bid increment to improve accuracy greatly increases the running time. In this paper, we present an algorithm that computes the outcome of the proxy auction by examining only the events that cause the proxy bidders to change their behaviors. This algorithm is much faster than the alternative, and computes exact solutions.

No abstract available.