Sat, 26 Jul 2008 04:34:52 BST CiteULike: V's library [127 articles] http://www.citeulike.org/user/V CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/V/article/2111841 <i>(2000)</i><br /><br />Relational data offer a unique opportunity for improving the classification accuracy of statistical models. If two objects are related, inferring something about one object can aid inferences about the other. We present an iterative classification procedure that exploits this characteristic of relational data. This approach uses simple Bayesian classifiers in an iterative fashion, dynamically updating the attributes of some objects as inferences are made about related objects. Inferences made... Iterative classification in relational data J Neville D Jensen (2000) 2007-12-14T06:29:46-00:00 2000 bayes classification http://www.citeulike.org/user/V/article/2820988 <i>(23 July 2008)</i> The Probabilistic Program Dependence Graph and its Application to Fault Diagnosis George Baah Andy Podgurski Mary Harrold (23 July 2008) 2008-05-21T18:19:00-00:00 2008 debugging dependencygraph statistics http://www.citeulike.org/user/V/article/2820969 <i>Machine Learning: ECML 2007 (2007), pp. 6-17.</i><br /><br />Statistical debugging uses machine learning to model program failures and help identify root causes of bugs. We approach this task using a novel Delta-Latent-Dirichlet-Allocation model. We model execution traces attributed to failed runs of a program as being generated by two types of latent topics: normal usage topics and bug topics. Execution traces attributed to successful runs of the same program, however, are modeled by usage topics only. Joint modeling of both kinds of traces allows us to identify weak bug topics that would otherwise remain undetected. We perform model inference with collapsed Gibbs sampling. In quantitative evaluations on four real programs, our model produces bug topics highly correlated to the true bugs, as measured by the Rand index. Qualitative evaluation by domain experts suggests that our model outperforms existing statistical methods for bug cause identification, and may help support other software tasks not addressed by earlier models. Statistical Debugging Using Latent Topic Models David Andrzejewski Anne Mulhern Ben Liblit Xiaojin Zhu doi:10.1007/978-3-540-74958-5_5 Machine Learning: ECML 2007 (2007), pp. 6-17. 2008-05-21T18:05:35-00:00 2007 Machine Learning: ECML 2007 6 17 debugging markov statistical http://www.citeulike.org/user/V/article/472852 <i>(2002), pp. 467-477.</i><br /><br />One of the most expensive and time-consuming components of the debugging process is locating the errors or faults. To locate faults, developers must identify statements involved in failures and select suspicious statements that might contain faults. This paper presents a new technique that uses visualization to assist with these tasks. The technique uses color to visually map the participation of each program statement in the outcome of the execution of the program with a test suite, consisting of both passed and failed test cases. Based on this visual mapping, a user can inspect the statements in the program, identify statements involved in failures, and locate potentially faulty statements. The paper also describes a prototype tool that implements our technique along with a set of empirical studies that use the tool for evaluation of the technique. The empirical studies show that, for the subject we studied, the technique can be effective in helping a user locate faults in a program. Visualization of test information to assist fault localization James Jones Mary Harrold John Stasko doi:10.1145/581339.581397 (2002), pp. 467-477. 2006-01-20T19:41:55-00:00 2002 467 477 ACM Press fault_localization sw_engineering http://www.citeulike.org/user/V/article/2749926 <i>(2007)</i> Xerces2 Java Parser The Apache Project (2007) 2008-05-03T19:26:59-00:00 2007 dataset fse08 proposal http://www.citeulike.org/user/V/article/2730957 <i></i> RBGL: An interface to the BOOST graph library Vince Carey Li Long R Gentleman 2008-04-28T20:16:31-00:00 eecs435 graph_theory rpkg tool http://www.citeulike.org/user/V/article/2730934 <i>(14 December 2007)</i> Semi-Supervised Learning Literature Survey Xiaojin Zhu (14 December 2007) 2008-04-28T20:05:07-00:00 2007 eecs435 semi-supervised_learning survey http://www.citeulike.org/user/V/article/2730868 <i>(2006)</i> The Java Interactive Profiler JIP Development Team (2006) 2008-04-28T19:30:19-00:00 2006 eecs435 java profiling proposal tool http://www.citeulike.org/user/V/article/2730859 <i>(2001), pp. 19-26.</i> Learning from Labeled and Unlabeled Data using Graph Mincuts Avrim Blum Shuchi Chawla (2001), pp. 19-26. 2008-04-28T19:26:43-00:00 2001 19 26 Morgan Kaufmann Publishers Inc. eecs435 semi-supervised_learning http://www.citeulike.org/user/V/article/2730857 <i>(2005)</i> ROME: RSS/Atom syndication and publishing tools ROME Development Team (2005) 2008-04-28T19:25:41-00:00 2005 dataset eecs435 fse08 proposal http://www.citeulike.org/user/V/article/104300 <i>(2004)</i> Semi-supervised learning using randomized mincuts Avrim Blum John Lafferty Mugizi Rwebangira Rajashekar Reddy doi:10.1145/1015330.1015429 (2004) 2005-02-25T22:20:40-00:00 2004 ACM Press eecs435 semi-supervised_learning http://www.citeulike.org/user/V/article/2683417 <i>(1998)</i><br /><br />Today's fault management is characterized by inefficient event management. The events delivered by the managed system frequently descibe symptoms of a problem instead of its cause. If a problem in the managed system occurs, e.g. a network failure or misconfigured software, the administrator often is flooded by a burst of more or less meaningless events indicating symptoms of the problem. The aim of an event correlator is to reduce the number and enrich the meaning of events shown to the... A New Approach for Event Correlation based on Dependency Graphs B Gruschke (1998) 2008-04-17T20:20:42-00:00 1998 dependencygraph event_correlation fse08 issre07 proposal http://www.citeulike.org/user/V/article/2683381 <i>Communications, IEEE Transactions on, Vol. 42, No. 234. (1994), pp. 523-533.</i><br /><br />Presents an approach for modeling and solving the problem of fault identification and alarm correlation in large communication networks. A single fault in a large network may result in a large number of alarms, and it is often very difficult to isolate the true cause of the fault. This appears to be one of the most important difficulties in managing faults in today's networks. The problem may become worse in the case of multiple faults. The authors present a general methodology for solving the alarm correlation and fault identification problem. They propose a new alarm structure, propose a general model for representing the network, and give two algorithms which can solve the alarm correlation and fault identification problem in the presence of multiple faults. These algorithms differ in the degree of accuracy achieved in identifying the fault, and in the degree of complexity required for implementation Alarm correlation and fault identification in communication networks AT Bouloutas S Calo A Finkel doi:10.1109/TCOMM.1994.577079 Communications, IEEE Transactions on, Vol. 42, No. 234. (1994), pp. 523-533. 2008-04-17T20:09:26-00:00 1994 Communications, IEEE Transactions on 42 234 523 533 distributed event_correlation fse08 issre07 proposal http://www.citeulike.org/user/V/article/2683375 <i>Communications Magazine, IEEE, Vol. 34, No. 5. (1996), pp. 82-90.</i><br /><br />The authors describe a network management system and illustrate its application to managing a distributed database application on a complex enterprise network High speed and robust event correlation SA Yemini S Kliger E Mozes Y Yemini D Ohsie doi:10.1109/35.492975 Communications Magazine, IEEE, Vol. 34, No. 5. (1996), pp. 82-90. 2008-04-17T20:07:39-00:00 1996 Communications Magazine, IEEE 34 5 82 90 distributed event_correlation fse08 issre07 proposal http://www.citeulike.org/user/V/article/2683368 <i>Advanced Data Mining and Applications (2005), pp. 217-224.</i><br /><br />During the debugging of a program to be released, it is unnecessary and impractical for developers to check every failure execution. How to extract the typical ones from the vast set of failure executions is very important for reducing the debugging efforts. In this paper, a revised Markov model used to depict program behaviors is presented firstly. Based on this model, the dissimilarity of two profile matrixes is also defined. After separating the failure executions and non-failure executions into two different subsets, iterative partition clustering and a sampling strategy called priority-ranked n-per-cluster are employed to extract representative failure executions. Finally, with the assistance of our prototype CppTest, we have performed experiment on five subject programs. The results show that the clustering and sampling techniques based on revised Markov model is more effective to find faults than Podgurski’s method. Extracting the Representative Failure Executions via Clustering Analysis Based on Markov Profile Model Chengying Mao Yansheng Lu doi:10.1007/11527503_26 Advanced Data Mining and Applications (2005), pp. 217-224. 2008-04-17T20:02:25-00:00 2005 Advanced Data Mining and Applications 217 224 classification fse08 issre07 markov proposal http://www.citeulike.org/user/V/article/1135329 <i>(2006), pp. 46-56.</i><br /><br />Recent software systems usually feature an automated failure reporting system, with which a huge number of failing traces are collected every day. In order to prioritize fault diagnosis, failing traces due to the same fault are expected to be grouped together. Previous methods, by hypothesizing that similar failing traces imply the same fault, cluster failing traces based on the literal trace similarity, which we call trace proximity. However, since a fault can be triggered in many ways, failing traces due to the same fault can be quite different. Therefore, previous methods actually group together traces exhibiting similar behaviors, like similar branch coverage, rather than traces due to the same fault. In this paper, we propose a new type of failure proximity, called R-Proximity, which regards two failing traces as similar if they suggest roughly the same fault location. The fault location each failing case suggests is automatically obtained with Sober, an existing statistical debugging tool. We show that with R-Proximity, failing traces due to the same fault can be grouped together. In addition, we find that R-Proximity is helpful for statistical debugging: It can help developers interpret and utilize the statistical debugging result. We illustrate the usage of R-Proximity with a case study on the grep program and some experiments on the Siemens suite, and the result clearly demonstrates the advantage of R-Proximity over trace proximity. Failure proximity: a fault localization-based approach Chao Liu Jiawei Han doi:10.1145/1181775.1181782 (2006), pp. 46-56. 2007-03-02T06:36:57-00:00 2006 46 56 ACM Press fault_localization fse08 issre07 proposal http://www.citeulike.org/user/V/article/2683339 <i>Dependable Systems and Networks, 2002. DSN 2002. Proceedings. International Conference on (2002), pp. 595-604.</i><br /><br />Traditional problem determination techniques rely on static dependency models that are difficult to generate accurately in today's large, distributed, and dynamic application environments such as e-commerce systems. We present a dynamic analysis methodology that automates problem determination in these environments by 1) coarse-grained tagging of numerous real client requests as they travel through the system and 2) using data mining techniques to correlate the believed failures and successes of these requests to determine which components are most likely to be at fault. To validate our methodology, we have implemented Pinpoint, a framework for root cause analysis on the J2EE platform that requires no knowledge of the application components. Pinpoint consists of three parts: a communications layer that traces client requests, a failure detector that uses traffic-sniffing and middleware instrumentation, and a data analysis engine. We evaluate Pinpoint by injecting faults into various application components and show that Pinpoint identifies the faulty components with high accuracy and produces few false-positives. Pinpoint: problem determination in large, dynamic Internet services MY Chen E Kiciman E Fratkin A Fox E Brewer doi:10.1109/DSN.2002.1029005 Dependable Systems and Networks, 2002. DSN 2002. Proceedings. International Conference on (2002), pp. 595-604. 2008-04-17T19:53:09-00:00 2002 Dependable Systems and Networks, 2002. DSN 2002. Proceedings. International Conference on 595 604 anomaly_detection distributed fse08 issre07 proposal testing http://www.citeulike.org/user/V/article/2683317 <i>(2007), pp. 101-111.</i><br /><br />An error occurs when software cannot complete a requested action as a result of some problem with its input, configuration, or environment. A high-quality error report allows a user to understand and correct the problem. Unfortunately, the quality of error reports has been decreasing as software becomes more complex and layered. End-users take the cryptic error messages given to them by programsand struggle to fix their problems using search engines and support websites. Developers cannot improve their error messages when they receive an ambiguous or otherwise insufficient error indicator from a black-box software component. We introduce Clarify, a system that improves error reporting by classifying application behavior. Clarify uses minimally invasive monitoring to generate a behavior profile, which is a summary of the program's execution history. A machine learning classifier uses the behavior profile to classify the application's behavior, thereby enabling a more precise error report than the output of the application itself. We evaluate a prototype Clarify system on ambiguous error messages generated by large, modern applications like gcc, La-TeX, and the Linux kernel. For a performance cost of less than 1% on user applications and 4.7% on the Linux kernel, the proto type correctly disambiguates at least 85% of application behaviors that result in ambiguous error reports. This accuracy does not degrade significantly with more behaviors: a Clarify classifier for 81 La-TeX error messages is at most 2.5% less accurate than a classifier for 27 LaTeX error messages. Finally, we show that without any human effort to build a classifier, Clarify can provide nearest-neighbor software support, where users who experience a problem are told about 5 other users who might have had the same problem. On average 2.3 of the 5 users that Clarify identifies have experienced the same problem. Improved error reporting for software that uses black-box components Jungwoo Ha Christopher Rossbach Jason Davis Indrajit Roy Hany Ramadan Donald Porter David Chen Emmett Witchel doi:10.1145/1250734.1250747 (2007), pp. 101-111. 2008-04-17T19:49:22-00:00 2007 101 111 ACM beta_testing fse08 proposal sw_engineering http://www.citeulike.org/user/V/article/2683281 <i>(2005), pp. 412-421.</i><br /><br />Some software defects trigger failures only when certain complex information flows occur within the software. Profiling and analyzing such flows therefore provides a potentially important basis for filtering test cases. We report the results of an empirical evaluation of several test case filtering techniques that are based on exercising complex information flows. Both coverage-based and profile-distribution-based filtering techniques are considered. They are compared to filtering techniques based on exercising basic blocks, branches, function calls, and def-use pairs, with respect to their effectiveness for revealing defects. An empirical evaluation of test case filtering techniques based on exercising complex information flows David Leon Wes Masri Andy Podgurski doi:10.1145/1062455.1062531 (2005), pp. 412-421. 2008-04-17T19:39:39-00:00 2005 412 421 ACM fse08 information_flow issre07 proposal testing http://www.citeulike.org/user/V/article/465806 <i>(30 September 2005)</i> What Is Web 2.0: Design Patterns and Business Models for the Next Generation of Software Tim O'Reilly (30 September 2005) 2006-01-15T22:39:46-00:00 2005 O'Reilly Media, Inc fse08 news proposal web20 http://www.citeulike.org/user/V/article/2682924 <i>(11 February 2005)</i> A long winding road out of beta Paul Festa (11 February 2005) 2008-04-17T16:48:44-00:00 2005 ZDNet beta_testing fse08 news proposal http://www.citeulike.org/user/V/article/2682917 <i>(23 August 2002)</i><br /><br />Implement, operate, and use beta testing immediately with this hands-on guide to the best practices <P>Beta testing is a complex process that, when properly run, provides a wealth of diverse information. But when poorly executed, it delivers little or no data while wasting time and money. Written by a leading expert in the field, this book will help you reach the full potential that beta testing has to offer. <P>Michael Fine compiles the best practices to date so you can effectively bring beta testing into your company’s process to improve product quality. Using real-world case studies, this book begins by clearly explaining what a beta is and why you need one. Fine then explores the beta test procedure and walks through the best processes to use when implementing a test. He concludes by detailing the steps you should take after completing a test in order to take full advantage of the results. <P>With this book, you’ll gain a better understanding of what beta testing is, why every company needs a beta test program, and how to get the most from a test. Fine will help you: <UL> <LI>Understand all the steps involved in beta testing using real-world case studies</LI> <LI>Implement a beta test using best- known practices</LI> <LI>Produce better products based on the results of well-run beta tests</LI> <LI>Apply beta testing across many platforms and many technologies</LI> <LI>Improve on existing processes and identify critical issues</LI></UL> Beta Testing for Better Software Michael Fine (23 August 2002) 2008-04-17T16:44:30-00:00 2002 Wiley beta_testing fse08 proposal sw_engineering http://www.citeulike.org/user/V/article/2682599 <i>SIGSOFT Softw. Eng. Notes, Vol. 25, No. 5. (September 2000), pp. 158-167.</i><br /><br />We describe the design of jRapture: a tool for capturing and replaying Java program executions in the field. jRapture works with Java binaries (byte code) and any compliant implementation of the Java virtual machine. It employs a lightweight, transparent capture process that permits unobtrusive capture of a Java programs executions. jRapture captures interactions between a Java program and the system, including GUI, file, and console inputs, among other types, and on replay it presents each thread with exactly the same input sequence it saw during capture. In addition, jRapture has a profiling interface that permits a Java program to be instrumented for profiling ó after its executions have been captured. Using an XML-based profiling specification language a tester can specify various forms of profiling to be carried out during replay. jRapture: A Capture/Replay tool for observation-based testing John Steven Pravir Chandra Bob Fleck Andy Podgurski doi:10.1145/347636.348993 SIGSOFT Softw. Eng. Notes, Vol. 25, No. 5. (September 2000), pp. 158-167. 2008-04-17T15:48:56-00:00 2000 SIGSOFT Softw. Eng. Notes 0163-5948 25 5 158 167 ACM fse08 issre07 java obt proposal testing http://www.citeulike.org/user/V/article/2682567 <i></i> knnFinder: Fast Near Neighbour Search Samuel Kemp 2008-04-17T15:35:28-00:00 eecs435 fse08 proposal rpkg tool http://www.citeulike.org/user/V/article/2682535 <i>(2008)</i> R: A Language and Environment for Statistical Computing R Development Core Team (2008) 2008-04-17T15:27:51-00:00 2008 eecs435 fse08 issre07 proposal tool http://www.citeulike.org/user/V/article/2682512 <i></i> GCC, the GNU Compiler Collection GNU Project 2008-04-17T15:23:36-00:00 dataset issre07 proposal http://www.citeulike.org/user/V/article/2682508 <i>(2002)</i> Jacks Project The Mauve Project (2002) 2008-04-17T15:21:30-00:00 2002 dataset fse08 issre07 java proposal testing http://www.citeulike.org/user/V/article/2682339 <i>(1998-2000)</i> Java 2 Platform, Standard Edition 1.3 (1998-2000) 2008-04-17T15:15:28-00:00 dataset fse08 issre07 java proposal http://www.citeulike.org/user/V/article/2682323 <i>(1998-2000)</i> JTidy (1998-2000) 2008-04-17T15:12:59-00:00 dataset fse08 issre07 java proposal http://www.citeulike.org/user/V/article/2682305 <i>ACM Trans. Softw. Eng. Methodol., Vol. 8, No. 3. (July 1999), pp. 263-283.</i><br /><br />A new approach to software reliability estimation is presented that combines operational testing with stratified sampling in order to reduce the number of program executions that must be checked manually for conformance to requirements. Automatic cluster analysis is applied to execution profiles in order to stratify captured operational executions. Experimental results are reported that suggest this approach can significantly reduce the cost of estimating reliability. Estimation of software reliability by stratified sampling Andy Podgurski Wassim Masri Yolanda Mccleese Francis Wolff Charles Yang doi:10.1145/310663.310667 ACM Trans. Softw. Eng. Methodol., Vol. 8, No. 3. (July 1999), pp. 263-283. 2008-04-17T15:06:29-00:00 1999 ACM Trans. Softw. Eng. Methodol. 8 3 263 283 ACM obt proposal reliability statistics http://www.citeulike.org/user/V/article/2682285 <i>(2005), pp. 1-7.</i><br /><br />In this paper, we present a technique for selective capture and replay of program executions. Given an application, the technique allows for (1) selecting a subsystem of interest, (2) capturing at runtime all the interactions between such subsystem and the rest of the application, and (3) replaying the recorded interactions on the subsystem in isolation. The technique can be used in several scenarios. For example, it can be used to generate test cases from users' executions, by capturing and collecting partial executions in the field. For another example. it can be used to perform expensive dynamic analyses off-line. For yet another example, it can be used to extract subsystem or unit tests from system tests. Our technique is designed to be efficient, in that we only capture information that is relevant to the considered execution. To this end, we disregard all data that, although flowing through the boundary of the subsystem of interest, do not affect the execution. In the paper, we also present a preliminary evaluation of the technique performed using SCARPE, a prototype tool that implements our approach. Selective capture and replay of program executions Alessandro Orso Bryan Kennedy doi:10.1145/1083246.1083251 (2005), pp. 1-7. 2008-04-17T15:00:40-00:00 2005 1 7 ACM obt profiling proposal testing http://www.citeulike.org/user/V/article/2678768 <i>(2006), pp. 85-91.</i><br /><br />Testing is a major cost factor in software development. Test automation has been proposed as one solution to reduce these costs. Test automation tools promise to increase the number of tests they run and the frequency at which they run them. So why not automate every test? In this paper we discuss the question "When should a test be automated?" and the trade-off between automated and manual testing. We reveal problems in the overly simplistic cost models commonly used to make decisions about automating testing. We introduce an alternative model based on opportunity cost and present influencing factors on the decision of whether or not to invest in test automation. Our aim is to stimulate discussion about these factors as well as their influence on the benefits and costs of automated testing in order to support researchers and practitioners reflecting on proposed automation approaches. Economic perspectives in test automation: balancing automated and manual testing with opportunity cost Rudolf Ramler Klaus Wolfmaier doi:10.1145/1138929.1138946 (2006), pp. 85-91. 2008-04-16T19:14:11-00:00 2006 85 91 ACM automation economics testing http://www.citeulike.org/user/V/article/745875 <i>(2003), pp. 141-154.</i><br /><br />We propose a low-overhead sampling infrastructure for gathering information from the executions experienced by a program's user community. Several example applications illustrate ways to use sampled instrumentation to isolate bugs. Assertion-dense code can be transformed to share the cost of assertions among many users. Lacking assertions, broad guesses can be made about predicates that predict program errors and a process of elimination used to whittle these down to the true bug. Finally, even for non-deterministic bugs such as memory corruption, statistical modeling based on logistic regression allows us to identify program behaviors that are strongly correlated with failure and are therefore likely places to look for the error. Bug isolation via remote program sampling Ben Liblit Alex Aiken Alice Zheng Michael Jordan doi:10.1145/781131.781148 (2003), pp. 141-154. 2006-07-07T14:31:20-00:00 2003 0362-1340 141 154 ACM Press proposal sw_engineering testing http://www.citeulike.org/user/V/article/2679226 <i>Software Reliability Engineering, 2004. ISSRE 2004. 15th International Symposium on (2004), pp. 451-462.</i><br /><br />Recent research has addressed the problem of providing automated assistance to software developers in classifying reported instances of software failures so that failures with the same cause are grouped together. In this paper, two new tree-based techniques are presented for refining an initial classification of failures. One of these techniques is based on the use of dendrograms, which are rooted trees used to represent the results of hierarchical cluster analysis. The second technique employs a classification tree constructed to recognize failed executions. With both techniques, the tree representation is used to guide the refinement process. We also report the results of experimentally evaluating these techniques on several subject programs. Tree-based methods for classifying software failures P Francis D Leon M Minch A Podgurski doi:10.1109/ISSRE.2004.43 Software Reliability Engineering, 2004. ISSRE 2004. 15th International Symposium on (2004), pp. 451-462. 2008-04-17T00:22:12-00:00 2004 Software Reliability Engineering, 2004. ISSRE 2004. 15th International Symposium on 451 462 obt proposal sw_engineering testing http://www.citeulike.org/user/V/article/2679215 <i>Software Reliability Engineering, 2003. ISSRE 2003. 14th International Symposium on (2003), pp. 442-453.</i><br /><br />This paper presents an empirical comparison of four different techniques for filtering large test suites: test suite minimization, prioritization by additional coverage, cluster filtering with one-per-cluster sampling, and failure pursuit sampling. The first two techniques are based on selecting subsets that maximize code coverage as quickly as possible, while the latter two are based on analyzing the distribution of the tests' execution profiles. These techniques were compared with data sets obtained from three large subject programs: the GCC, Jikes, and javac compilers. The results indicate that distribution-based techniques can be as efficient or more efficient for revealing defects than coverage-based techniques, but that the two kinds of techniques are also complementary in the sense that they find different defects. Accordingly, some simple combinations of these techniques were evaluated for use in test case prioritization. The results indicate that these techniques can create more efficient prioritizations than those generated using prioritization by additional coverage. A comparison of coverage-based and distribution-based techniques for filtering and prioritizing test cases D Leon A Podgurski doi:10.1109/ISSRE.2003.1251065 Software Reliability Engineering, 2003. ISSRE 2003. 14th International Symposium on (2003), pp. 442-453. 2008-04-17T00:15:59-00:00 2003 Software Reliability Engineering, 2003. ISSRE 2003. 14th International Symposium on 442 453 obt proposal sw_engineering testing http://www.citeulike.org/user/V/article/2253110 <i>Software Reliability Engineering, 2004. ISSRE 2004. 15th International Symposium on (2004), pp. 198-209.</i><br /><br />A new approach to dynamic information flow analysis is presented that can be used to detect and debug insecure flows in programs. It can be applied offline to validate and debug a program against an information flow policy, or, when fast response is not critical, it can be applied online to prevent illegal flows in deployed programs. Since dynamic analysis alone is inherently unable to detect implicit information flows, our approach incorporates a static preprocessing phase that permits detection of most implicit flows at runtime, in addition to explicit ones. To support interactive debugging of insecure flows, it also incorporates a new forward computing algorithm for dynamic slicing, which is more precise than previous forward computing algorithms and is not restricted to programs with structured control flow. A prototype tool implementing the proposed approach has been developed for Java byte code programs. Case studies in which this tool was applied to several subject programs are described. Detecting and debugging insecure information flows W Masri A Podgurski D Leon doi:10.1109/ISSRE.2004.17 Software Reliability Engineering, 2004. ISSRE 2004. 15th International Symposium on (2004), pp. 198-209. 2008-01-18T19:41:50-00:00 2004 Software Reliability Engineering, 2004. ISSRE 2004. 15th International Symposium on 198 209 information_flow obt sw_engineering testing http://www.citeulike.org/user/V/article/259537 <i>Software Maintenance, 2004. Proceedings. 20th IEEE International Conference on (2004), pp. 188-197.</i><br /><br />This paper describes an automated tool called Dex (difference extractor) for analyzing syntactic and semantic changes in large C-language code bases. It is applied to patches obtained from a source code repository, each of which comprises the code changes made to accomplish a particular task. Dex produces summary statistics characterizing these changes for all of the patches that are analyzed. Dex applies a graph differencing algorithm to abstract semantic graphs (ASGs) representing each version. The differences are then analyzed to identify higher-level program changes. We describe the design of Dex, its potential applications, and the results of applying it to analyze bug fixes from the Apache and GCC projects. The results include detailed information about the nature and frequency of missing condition defects in these projects. Dex: a semantic-graph differencing tool for studying changes in large code bases S Raghavan R Rohana D Leon A Podgurski V Augustine doi:10.1109/ICSM.2004.1357803 Software Maintenance, 2004. Proceedings. 20th IEEE International Conference on (2004), pp. 188-197. 2005-07-19T15:04:21-00:00 2004 Software Maintenance, 2004. Proceedings. 20th IEEE International Conference on 188 197 analysis graph_theory source http://www.citeulike.org/user/V/article/2253101 <i>Software Engineering, 2000. Proceedings of the 2000 International Conference on (2000), pp. 116-125.</i><br /><br />We explore the use of multivariate visualization techniques to support a new approach to test data selection, called observation-based testing. Applications of multivariate visualization are described, including: evaluating and improving synthetic tests; filtering regression test suites; filtering captured operational executions; comparing test suites; and assessing bug reports. These applications are illustrated by the use of correspondence analysis to analyze test inputs for the GNU GCC compiler Multivariate visualization in observation-based testing D Leon A Podgurski LJ White doi:10.1109/ICSE.2000.870403 Software Engineering, 2000. Proceedings of the 2000 International Conference on (2000), pp. 116-125. 2008-01-18T19:40:13-00:00 2000 Software Engineering, 2000. Proceedings of the 2000 International Conference on 116 125 obt sw_engineering testing http://www.citeulike.org/user/V/article/2253086 <i>(2001), pp. 246-255.</i><br /><br />Observation-based testing calls for analyzing profiles of executions induced by potential test cases, in order to select a subset of executions to be checked for conformance to requirements. A family of techniques for selecting such a subset is evaluated experimentally. These techniques employ automatic cluster analysis to partition executions, and they use various sampling techniques to select executions from clusters. The experimental results support the hypothesis that with appropriate profiling, failures often have unusual profiles that are revealed by cluster analysis. The results also suggest that failures often form small clusters or chains in sparsely-populated areas of the profile space. A form of adaptive sampling called failure-pursuit sampling is proposed for revealing failures in such regions, and this sampling method is evaluated experimentally. The results suggest that failure-pursuit sampling is effective. Pursuing failure: the distribution of program failures in a profile space William Dickinson David Leon Andy Podgurski doi:10.1145/503209.503243 (2001), pp. 246-255. 2008-01-18T19:35:34-00:00 2001 246 255 ACM obt proposal sw_engineering testing http://www.citeulike.org/user/V/article/2253083 <i>(2003), pp. 465-475.</i><br /><br />This paper proposes automated support for classifying reported software failures in order to facilitate prioritizing them and diagnosing their causes. A classification strategy is presented that involves the use of supervised and unsupervised pattern classification and multivariate visualization. These techniques are applied to profiles of failed executions in order to group together failures with the same or similar causes. The resulting classification is then used to assess the frequency and severity of failures caused by particular defects and to help diagnose those defects. The results of applying the proposed classification strategy to failures of three large subject programs are reported. These results indicate that the strategy can be effective. Automated support for classifying software failure reports Andy Podgurski David Leon Patrick Francis Wes Masri Melinda Minch Jiayang Sun Bin Wang (2003), pp. 465-475. 2008-01-18T19:34:36-00:00 2003 465 475 IEEE Computer Society obt proposal sw_engineering testing http://www.citeulike.org/user/V/article/2253071 <i>(2001), pp. 339-348.</i><br /><br />We experimentally evaluate the effectiveness of using cluster analysis of execution profiles to find failures among the executions induced by a set of potential test cases. We compare several filtering procedures for selecting executions to evaluate for conformance to requirements. Each filtering procedure involves a choice of a sampling strategy and a clustering metric. The results suggest that filtering procedures based on clustering are more effective than simple random sampling for identifying failures in populations of operational executions, with adaptive sampling from clusters being the most effective sampling strategy. The results also suggest that clustering metrics that give extra weight to unusual profile features are most effective. Scatter plots of execution populations, produced by multidimensional scaling, are used to provide intuition for these results. Finding failures by cluster analysis of execution profiles William Dickinson David Leon Andy Podgurski (2001), pp. 339-348. 2008-01-18T19:31:47-00:00 2001 339 348 IEEE Computer Society obt proposal sw_engineering testing http://www.citeulike.org/user/V/article/2253067 <i>Software Reliability, 2007. ISSRE '07. The 18th IEEE International Symposium on (2007), pp. 61-70.</i><br /><br />Operational or "beta" testing of software has a number of benefits for software vendors and has become common industry practice. However, ordinary users are more likely to overlook or misreport software problems than experienced software testers are. To compensate for this shortcoming, we present a technique called corroboration-based filtering for corroborating user assessments of individual operational executions for which audit information has been captured for possible offline review. Independent assessments concerning similar executions are pooled by automatically clustering together executions with similar execution profiles. Executions are chosen for review based on their user assessments, the size of the cluster each execution belongs to, and whether the cluster has already been confirmed by developers to contain an actual failure. We explain the rationale for this technique, analyze it probabilistically, and present the results of empirically comparing it to alternative techniques. Corroborating User Assessments of Software Behavior to Facilitate Operational Testing Vinay Augustine Andy Podgurski doi:10.1109/ISSRE.2007.7 Software Reliability, 2007. ISSRE '07. The 18th IEEE International Symposium on (2007), pp. 61-70. 2008-01-18T19:29:43-00:00 2007 Software Reliability, 2007. ISSRE '07. The 18th IEEE International Symposium on 61 70 corroboration eecs435 proposal sw_engineering testing http://www.citeulike.org/user/V/article/2204598 <i>(2005), pp. 107-116.</i><br /><br />In this paper, we propose a method to help users avoid bugs in GUI applications. In particular, users would use the application normally and report bugs that they encounter to prevent anyone -- including themselves -- from encountering those bugs again. When a user attempts an action that has led to problems in the past, he/she will receive a warning and will be given the opportunity to abort the action -- thus avoiding the bug altogether and keeping the application stable. Of course, bugs should be fixed eventually by the application developers, but our approach allows application users to collaboratively help each other avoid bugs -- thus making the application more usable in the meantime. We demonstrate this approach using our "Stabilizer" prototype. We also include a preliminary evaluation of the Stabilizer's bug prediction. Helping users avoid bugs in GUI applications Amir Michail Tao Xie (2005), pp. 107-116. 2008-01-07T18:35:21-00:00 2005 107 116 ACM proposal sw_engineering testing http://www.citeulike.org/user/V/article/2204587 <i>(2006), pp. 169-180.</i><br /><br />Given an incorrect value produced during a failed program run (e.g., a wrong output value or a value that causes the program to crash), the backward dynamic slice of the value very frequently captures the faulty code responsible for producing the incorrect value. Although the dynamic slice often contains only a small percentage of the statements executed during the failed program run, the dynamic slice can still be large and thus considerable effort may be required by the programmer to locate the faulty code.In this paper we develop a strategy for pruning the dynamic slice to identify a subset of statements in the dynamic slice that are likely responsible for producing the incorrect value. We observe that some of the statements used in computing the incorrect value may also have been involved in computing correct values (e.g., a value produced by a statement in the dynamic slice of the incorrect value may also have been used in computing a correct output value prior to the incorrect value). For each such executed statement in the dynamic slice, using the value profiles of the executed statements, we compute a confidence value ranging from 0 to 1 - a higher confidence value corresponds to greater likelihood that the execution of the statement produced a correct value. Given a failed run involving execution of a single error, we demonstrate that the pruning of a dynamic slice by excluding only the statements with the confidence value of 1 is highly effective in reducing the size of the dynamic slice while retaining the faulty code in the slice. Our experiments show that the number of distinct statements in a pruned dynamic slice are 1.79 to 190.57 times less than the full dynamic slice. Confidence values also prioritize the statements in the dynamic slice according to the likelihood of them being faulty. We show that examining the statements in the order of increasing confidence values is an effective strategy for reducing the effort of fault location. Pruning dynamic slices with confidence Xiangyu Zhang Neelam Gupta Rajiv Gupta (2006), pp. 169-180. 2008-01-07T18:27:30-00:00 2006 0362-1340 169 180 ACM information_flow slicing toread http://www.citeulike.org/user/V/article/2204567 <i>(2006), pp. 6-13.</i><br /><br />An oracle in software testing is a mechanism for checking whether the system under test has behaved correctly for any executions. In some situations, oracles are unavailable or too expensive to apply. This is known as the oracle problem. It is crucial to develop techniques to address it, and metamorphic testing (MT) was one of such proposals. This paper conducts a controlled experiment to investigate the cost effectiveness of using MT by 38 testers on three open-source programs. The fault detection capability and time cost of MT are compared with the popular assertion checking method. Our results show that MT is cost-efficient and has potentials for detecting more faults than the assertion checking method. An empirical comparison between direct and indirect test result checking approaches Peifeng Hu Zhenyu Zhang WK Chan TH Tse (2006), pp. 6-13. 2008-01-07T18:19:47-00:00 2006 6 13 ACM sw_engineering testing toread http://www.citeulike.org/user/V/article/329575 <i>SIGKDD Explor. Newsl., Vol. 6, No. 1. (June 2004), pp. 90-105.</i> Subspace clustering for high dimensional data: a review Lance Parsons Ehtesham Haque Huan Liu doi:10.1145/1007730.1007731 SIGKDD Explor. Newsl., Vol. 6, No. 1. (June 2004), pp. 90-105. 2005-09-22T04:58:48-00:00 2004 SIGKDD Explor. Newsl. 6 1 90 105 ACM Press algorithms classification data_mining high_dimensional_space survey toread http://www.citeulike.org/user/V/article/2057345 <i>ACM Queue, Vol. 2, No. 8. (November 2004)</i><br /><br />There are many ways to measure quality before and after software is released. For commercial and internal-use-only products, the most important measurement is the user’s perception of product quality. Unfortunately, perception is difficult to measure, so companies attempt to quantify it through customer satisfaction surveys and failure/behavioral data collected from its customer base. This article focuses on the problems of capturing failure data from customer sites. To explore the pertinent issues I rely on experience gained from collecting failure data from Windows XP systems, but the problems you are likely to face when developing internal (noncommercial) software should not be dissimilar. Automating Software Failure Reporting: We can only fix those bugs we know about. B Murphy ACM Queue, Vol. 2, No. 8. (November 2004) 2007-12-04T15:17:16-00:00 2004 ACM Queue 2 8 beta_testing obt statistics http://www.citeulike.org/user/V/article/1559292 <i>(2007), pp. 239-249.</i> Heuristic ranking of java program edits for fault localization Xiaoxia Ren Barbara Ryder doi:10.1145/1273463.1273495 (2007), pp. 239-249. 2007-08-14T01:47:51-00:00 2007 239 249 ACM Press fault_localization toread http://www.citeulike.org/user/V/article/1559288 <i>Software Engineering, IEEE Transactions on, Vol. 33, No. 5. (2007), pp. 287-304.</i><br /><br />There is an increasing interest in techniques that support analysis and measurement of fielded software systems. These techniques typically deploy numerous instrumented instances of a software system, collect execution data when the instances run in the field, and analyze the remotely collected data to better understand the system's in-the-field behavior. One common need for these techniques is the ability to distinguish execution outcomes (e.g., to collect only data corresponding to some behavior or to determine how often and under which condition a specific behavior occurs). Most current approaches, however, do not perform any kind of classification of remote executions and either focus on easily observable behaviors (e.g., crashes) or assume that outcomes' classifications are externally provided (e.g., by the users). To address the limitations of existing approaches, we have developed three techniques for automatically classifying execution data as belonging to one of several classes. In this paper, we introduce our techniques and apply them to the binary classification of passing and failing behaviors. Our three techniques impose different overheads on program instances and, thus, each is appropriate for different application scenarios. We performed several empirical studies to evaluate and refine our techniques and to investigate the trade-offs among them. Our results show that 1) the first technique can build very accurate models, but requires a complete set of execution data; 2) the second technique produces slightly less accurate models, but needs only a small fraction of the total execution data; and 3) the third technique allows for even further cost reductions by building the models incrementally, but requires some sequential ordering of the software instances' instrumentation. Techniques for Classifying Executions of Deployed Software to Support Software Engineering Tasks Murali Haran Alan Karr Michael Last Alessandro Orso Porter Ashish Sanil Sandro Fouche Software Engineering, IEEE Transactions on, Vol. 33, No. 5. (2007), pp. 287-304. 2007-08-14T01:43:11-00:00 2007 Software Engineering, IEEE Transactions on 33 5 287 304 beta_testing classification sw_engineering http://www.citeulike.org/user/V/article/1559277 <i>(2006), pp. 209-226.</i><br /><br />In this chapter, we present an algorithmic framework for semi-supervised inference based on geometric properties of probability distributions. Our approach brings together Laplacian-based spectral techniques, regularization with kernel methods, and algorithms for manifold learning. This framework provides a natural semi-supervised extension for kernel methods and resolves the problem of out-of-sample inference in graph-based transduction. We discuss an interpretation in terms of a family of globally defined data-dependent kernels and also address unsupervised learning (clustering and data representation) within the same framework. Our algorithms effectively exploit both manifold and cluster assumptions to demonstrate state-of-the-art performance on various classification tasks. This chapter also reviews other recent work on out-of-sample extension for transductive graph-based methods. Geometric Basis of Semi-supervised Learning Vikas Sindhwani Misha Belkin Partha Niyogi (2006), pp. 209-226. 2007-08-14T01:35:36-00:00 2006 209 226 MIT Press semi-supervised_learning