Madeline Diep, Sebastian G. Elbaum, Matthew B. Dwyer
{"title":"Trace Normalization","authors":"Madeline Diep, Sebastian G. Elbaum, Matthew B. Dwyer","doi":"10.1109/ISSRE.2008.37","DOIUrl":"https://doi.org/10.1109/ISSRE.2008.37","url":null,"abstract":"Identifying truly distinct traces is crucial for the performance of many dynamic analysis activities. For example, given a set of traces associated with a program failure, identifying a subset of unique traces can reduce the debugging effort by producing a smaller set of candidate fault locations. The process of identifying unique traces, however, is subject to the presence of irrelevant variations in the sequence of trace events, which can make a trace appear unique when it is not. In this paper we present an approach to reduce inconsequential and potentially detrimental trace variations. The approach decomposes traces into segments on which irrelevant variations caused by event ordering or repetition can be identified, and then used to normalize the traces in the pool. The approach is investigated on two well-known client dynamic analyses by replicating the conditions under which they were originally assessed, revealing that the clients can deliver more precise results with the normalized traces.","PeriodicalId":448275,"journal":{"name":"2008 19th International Symposium on Software Reliability Engineering (ISSRE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130060625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Syed Shariyar Murtaza, Mechelle Gittens, N. Madhavji
{"title":"Discovering the Fault Origin from Field Traces","authors":"Syed Shariyar Murtaza, Mechelle Gittens, N. Madhavji","doi":"10.1109/ISSRE.2008.57","DOIUrl":"https://doi.org/10.1109/ISSRE.2008.57","url":null,"abstract":"This paper proposes an automatic technique to reduce the time spent in detection of the fault origin from field traces, by discovering hidden patterns in the traces.","PeriodicalId":448275,"journal":{"name":"2008 19th International Symposium on Software Reliability Engineering (ISSRE)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117204450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Effect of the Number of Defects on Estimates Produced by Capture-Recapture Models","authors":"G. Walia, Jeffrey C. Carver","doi":"10.1109/ISSRE.2008.61","DOIUrl":"https://doi.org/10.1109/ISSRE.2008.61","url":null,"abstract":"Project managers use inspection data as input to capture-recapture (CR) models to estimate the total number of faults present in a software artifact. The CR models use the number of faults found during an inspection and the overlap of faults among inspectors to calculate the estimate. A common belief is that CR models underestimate the number of faults but their performance can be improved with more input data. This paper investigates the minimum number of faults that has to be present in an artifact before the CR method can be used. The result shows that the minimum number of faults varies from ten faults to twenty-three faults for different CR estimators.","PeriodicalId":448275,"journal":{"name":"2008 19th International Symposium on Software Reliability Engineering (ISSRE)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115879738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using Measures and Risk Indicators for Early Insight into Software Product Characteristics such as Software Safety","authors":"V. Basili","doi":"10.1109/ISSRE.2008.66","DOIUrl":"https://doi.org/10.1109/ISSRE.2008.66","url":null,"abstract":"Software safety is one such product characteristic and this approach has been applied to identifying software safety insight areas and goals and developing early software safety measures, models and responses. Although the actual safety of a system cannot be verified during development, measures can reveal early insights into potential safety problems and risks. The approach and the example software measures presented are based on experience working with the safety engineering group on a large Department of Defense program.","PeriodicalId":448275,"journal":{"name":"2008 19th International Symposium on Software Reliability Engineering (ISSRE)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127120749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Testing of User-Configurable Software Systems Using Firewalls","authors":"Brian P. Robinson, L. White","doi":"10.1002/stvr.428","DOIUrl":"https://doi.org/10.1002/stvr.428","url":null,"abstract":"User-configurable software systems present many challenges to software testers. These systems are created to address a large number of possible uses, each of which is based on a specific configuration. As configurations are made up of groups of configurable elements and settings, a huge number of possible combinations exist. Since it is infeasible to test all configurations before release, many latent defects remain in the software once deployed. An incremental testing process is presented to address this problem, including examples of how it can be used with various user-configurable systems in the field. The proposed solution is evaluated with a set of empirical studies conducted on two separate ABB software systems using real customer configurations and changes. The three case studies analyzed failures reported by many different customers around the world and show that this incremental testing process is effective at detecting latent defects exposed by customer configuration changes in user-configurable systems.","PeriodicalId":448275,"journal":{"name":"2008 19th International Symposium on Software Reliability Engineering (ISSRE)","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115553256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Model-Driven Approach to Assuring Process Reliability","authors":"I. Habli, T. Kelly","doi":"10.1109/ISSRE.2008.19","DOIUrl":"https://doi.org/10.1109/ISSRE.2008.19","url":null,"abstract":"The process can fail to deliver its expected outputs and consequently contribute to the introduction of faults into the software system. The process may fail due to ambiguous and unsuitable notations, unreliable tool-support, flawed methods and techniques or incompetent personnel. However, not all process activities pose the same degree of risks and therefore require the same degree of rigour. In this paper, we define an extendable metamodel for describing lifecycle processes. The metamodel embodies attributes which facilitate the automated analysis of the process, revealing possible process failures and associated risks. The metamodel also provides the capability to automatically verify the compliance of the process with certification standards. The metamodel is evaluated against processes from the aerospace and automotive domains.","PeriodicalId":448275,"journal":{"name":"2008 19th International Symposium on Software Reliability Engineering (ISSRE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124311372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Automated Identification of Failure Causes in System Logs","authors":"L. Mariani, F. Pastore","doi":"10.1109/ISSRE.2008.48","DOIUrl":"https://doi.org/10.1109/ISSRE.2008.48","url":null,"abstract":"Log files are commonly inspected by system administrators and developers to detect suspicious behaviors and diagnose failure causes. Since size of log files grows fast, thus making manual analysis impractical, different automatic techniques have been proposed to analyze log files. Unfortunately, accuracy and effectiveness of these techniques are often limited by the unstructured nature of logged messages and the variety of data that can be logged.This paper presents a technique to automatically analyze log files and retrieve important information to identify failure causes. The technique automatically identifies dependencies between events and values in logs corresponding to legal executions, generates models of legal behaviors and compares log files collected during failing executions with the generated models to detect anomalous event sequences that are presented to users. Experimental results show the effectiveness of the technique in supporting developers and testers to identify failure causes.","PeriodicalId":448275,"journal":{"name":"2008 19th International Symposium on Software Reliability Engineering (ISSRE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131333441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Software Reliability - 40 Years of Avoiding the Question","authors":"Russell Morris","doi":"10.1109/ISSRE.2008.65","DOIUrl":"https://doi.org/10.1109/ISSRE.2008.65","url":null,"abstract":"Summary form only given. During the past years, there has been an explosion of technical complexity of both hardware and software. Hardware, characterized by physics and empirical data, provides the reliability and systems engineers with a capability to estimate the expected reliability. Software has however managed to avoid this type of model development in part because the factors affecting reliability are not measurable by physical data. Software reliability is characterized by data gathered during systems integration and test. This data has attributes and parameters such as defect density, capability of programming of the software engineering team, the experience of the engineering team, the understanding of the application be the designers, the language used and more. Software reliability is more than the processes advocated by CMMI (Capability Maturity Modelreg Integration) and is susceptible to esoteric and infinitely harder parameters to measure. The author discusses some of the elements that affect software reliability and compares some of the differences when trying to estimate reliability of today's systems.","PeriodicalId":448275,"journal":{"name":"2008 19th International Symposium on Software Reliability Engineering (ISSRE)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132416646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using Statistical Models to Predict Software Regressions","authors":"A. Tarvo","doi":"10.1109/ISSRE.2008.21","DOIUrl":"https://doi.org/10.1109/ISSRE.2008.21","url":null,"abstract":"Incorrect changes made to the stable parts of a software system can cause failures - software regressions. Early detection of faulty code changes can be beneficial for the quality of a software system when these errors can be fixed before the system is released. In this paper, a statistical model for predicting software regressions is proposed. The model predicts risk of regression for a code change by using software metrics: type and size of the change, number of affected components, dependency metrics, developerpsilas experience and code metrics of the affected components. Prediction results could be used to prioritize testing of changes: the higher is the risk of regression for the change, the more thorough testing it should receive.","PeriodicalId":448275,"journal":{"name":"2008 19th International Symposium on Software Reliability Engineering (ISSRE)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132464295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Seasonality in Vulnerability Discovery in Major Software Systems","authors":"Hyunchul Joh, Y. Malaiya","doi":"10.1109/ISSRE.2008.31","DOIUrl":"https://doi.org/10.1109/ISSRE.2008.31","url":null,"abstract":"Prediction of vulnerability discovery rates can be used to assess security risks and to determine the resources needed to develop patches quickly to handle vulnerabilities discovered. An examination of the vulnerability data suggests a seasonal behavior that has not been modeled by the recently proposed vulnerability discovery models. This seasonality has not been identified or examined so far. This study examines whether vulnerability discovery rates for Windows NT, IIS Server and the Internet Explorer exhibit a significant annual seasonal pattern. Actual data has been analyzed using seasonal index and auto correlation function approaches to identify seasonality and to evaluate its statistical significance. The results for the three software systems show that there is indeed a significant annual seasonal pattern.","PeriodicalId":448275,"journal":{"name":"2008 19th International Symposium on Software Reliability Engineering (ISSRE)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130030190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}