Software for multi-core processor-based architectures : Automatic detection of concurrency errors

The main objective of this paper is to propose a software implementation model for concurrency error detection tools that allows reducing process overhead without decreasing its detection capacity. The general model proposed uses software dynamic instrumentation in such a way that an analysis routine can be activated from a signal generated by a hardware event that indicates the possibility of an error occurring. The results obtained showed that, for the case study (an atomicity violation detection algorithm called AVIO), the version that uses the model proposed can detect the same bugs as the original version, but in only 25% of the time (in average) required by it.Es revisión de: http://sedici.unlp.edu.ar/handle/10915/44643Tesis presentada el 19 de marzo de 2015 por el autor para obtener el título de Doctor en Ciencias Informáticas.Facultad de Informátic

Software for multi-core processor-based architectures : Automatic detection of concurrency errors

The main objective of this paper is to propose a software implementation model for concurrency error detection tools that allows reducing process overhead without decreasing its detection capacity. The general model proposed uses software dynamic instrumentation in such a way that an analysis routine can be activated from a signal generated by a hardware event that indicates the possibility of an error occurring. The results obtained showed that, for the case study (an atomicity violation detection algorithm called AVIO), the version that uses the model proposed can detect the same bugs as the original version, but in only 25% of the time (in average) required by it.Tesis presentada el 19 de marzo de 2015 por el autor para obtener el título de Doctor en Ciencias Informáticas.Es revisión de: http://sedici.unlp.edu.ar/handle/10915/44643Facultad de Informátic

Automated Process Discovery: A Literature Review and a Comparative Evaluation with Domain Experts

Äriprotsesside kaeve meetodi võimaldavad analüütikul kasutada logisid saamaks teadmisi protsessi tegeliku toimise kohta. Neist meetodist üks enim uuritud on automaatne äriprotsesside avastamine. Sündmuste logi võetakse kui sisend automaatse äriprotsesside avastamise meetodi poolt ning väljundina toodetakse äriprotsessi mudel, mis kujutab logis talletatud sündmuste kontrollvoogu. Viimase kahe kümnendi jooksul on väljapakutud mitmeidki automaatseid äriprotsessi avastamise meetodeid balansseerides erinevalt toodetavate mudelite skaleeruvuse, täpsuse ning keerukuse vahel. Siiani on automaatsed äriprotsesside avastamise meetodid testitud ad-hoc kombel, kus erinevad autorid kasutavad erinevaid andmestike, seadistusi, hindamismeetrikuid ning alustõdesid, mis viib tihti võrdlematute tulemusteni ning mõnikord ka mittetaastoodetavate tulemusteni suletud andmestike kasutamise tõttu. Eelpool toodu mõistes sooritatakse antud magistritöö raames süstemaatiline kirjanduse ülevaade automaatsete äriprotsesside avastamise meetoditest ja ka süstemaatiline hindav võrdlus üle nelja kvaliteedimeetriku olemasolevate automaatsete äriprotsesside avastamise meetodite kohta koostöös domeeniekspertidega ning kasutades reaalset logi rahvusvahelisest tarkvara firmast. Kirjanduse ülevaate ning hindamise tulemused tõstavad esile puudujääke ning seni uurimata kompromisse mudelite loomiseks nelja kvaliteedimeetriku kontekstis. Antud magistritöö tulemused võimaldavad teaduritel parandada puudujäägid meetodites. Samuti vastatakse küsimusele automaatsete äriprotsesside avastamise meetodite kasutamise kohta väljaspool akadeemilist maailma.Process mining methods allow analysts to use logs of historical executions of business processes in order to gain knowledge about the actual performance of these processes.One of the most widely studied process mining operations is automated process discovery.An event log is taken as input by an automated process discovery method and produces a business process model as output that captures the control-flow relations between tasks that are described by the event log.Several automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models.So far, automated process discovery methods have been evaluated in an ad hoc manner, with different authors employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of non-publicly available datasets.In this setting, this thesis provides a systematic review of automated process discovery methods and a systematic comparative evaluation of existing implementations of these methods with domain experts by using a real-life event log extracted from a international software engineering company and four quality metrics.The review and evaluation results highlight gaps and unexplored tradeoffs in the field in the context of four business process model quality metrics.The results of this master thesis allows researchers to improve the lacks in the automated process discovery methods and also answers question about the usability of process discovery techniques in industry

Detecting Interleaving

The various goals and requirements of a system are realized in software as fragments of code that are typically "interleaved" in that they may be woven together in the same contiguous textual area of code. The fragments of code are often delocalized and overlap rather than being composed in a simple linear sequence. Interleaving severely complicates software comprehension and maintenance. To address this problem, we are developing analysis tools, based on the Software Refinery. This paper describes our experiences in detecting interleaving in a corpus of mathematical software written in Fortran from the Jet Propulsion Laboratory. In particular, it discusses how feasible it is to detect interleaving of various types and the ability of existing tools to assist these types of detection

Fuzzing with automatically controlled interleavings to detect concurrency bugs

Concurrency vulnerabilities are an irresistible threat to security, and detecting them is challenging. Triggering the concurrency vulnerabilities requires a specific thread interleaving and a bug-inducing input. Existing methods have focused on one of these but not both or have experimented with small programs, which raises scalability issues. This paper introduces AutoInter-fuzzing, a fuzzer controlling thread interleavings elaborately and providing an interleaving-aware power schedule to detect vulnerabilities in a multi-threaded program. AutoInter-fuzzing consists of static analysis and dynamic fuzzing. At the static analysis, the fuzzer extracts and optimizes the interleaving space to be explored and adds instrumentation to control thread interleavings. We apply the power schedule in the dynamic fuzzing to focus on the seeds that reveal the new interleaving space. The fuzzer records the interleaving information in a log when a crash occurs and uses it to reproduce and validate the crash. Experiments with 13 real-world multi-threaded programs show that the interleaving-aware power schedule effectively enlarges the untested interleaving space, and AutoInter-fuzzing outperforms AFL and ConAFL in detecting interleaving-relevant vulnerabilities. AutoInter-fuzzing has detected six interleaving-relevant vulnerabilities, including two new vulnerabilities and four interleaving-irrelevant vulnerabilities. (C) 2022 Elsevier Inc. All rights reserved.11Nsci