An Integrated Infrastructure Using Process Mining Techniques for Software Process Verification

Gürgen, Tuğba; Tarhan, Ayça; Karagöz, N. Alpay

doi:10.4018/978-1-5225-3923-0.ch061

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…This technique has been used for some years in software engineering. [33][34][35] It is applied to various software reengineering topics such as bug fixing. 36 Process mining is mainly used to reverse engineering of a work process.…”

Section: Related Workmentioning

confidence: 99%

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Data interoperability assessment, case of messaging‐based data exchanges

Laval

Niang

Derras³

et al. 2023

J Software Evolu Process

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Data interoperability implies data exchanges among intra and inter enterprises collaborating with information systems (IS). The multiplicity of these exchanges and the increasing number of data exchanged generates complexity and brings out the needs for control to avoid dysfunctions with a negative impact on the overall performance of the systems. Indeed, actually, interoperability has become a necessary performance lever that thus requires particular attention. Being at a low level in the enterprise interoperability concerns, data interoperability is mainly automated, which leads us to question: Is it possible to evaluate data interoperability performance and security based on inspection and analysis of ongoing data exchanges? We therefore endeavored to answer this question by establishing monitoring and analysis systems. In this paper, we present a research work which addresses services provided by a messaging‐based communication system. In order to collect information on Information System interactions allowing one to assess their level of data interoperability, we propose a messaging metamodel that aggregates the collected information. It provides a single point of control and enables one to determine indicators of potential interoperability problems. The approach is validated on two case studies. An industrial case study of interactions among existing systems is presented to showcase the feasibility and interest of the approach. It is proposed on top of RabbitMQ and allows our partner to identify some issues in the studied information system. The second case study shows that the approach can integrate other protocols, by reading MQTT messages. The approach is implemented using Moose, a software analysis platform.

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Section: Related Workmentioning

confidence: 99%

“…Collecting event log data to understand or reengineer a complex process is known as process mining. This technique has been used for some years in software engineering 33–35 . It is applied to various software reengineering topics such as bug fixing 36 .…”

Section: Related Workmentioning

confidence: 99%

Data interoperability assessment, case of messaging‐based data exchanges

Laval

Niang

Derras³

et al. 2023

J Software Evolu Process

View full text Add to dashboard Cite

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Monitoring the software bug‐fixing process through the process mining approach

Özdağoğlu

Kavuncubaşı

2019

J Software Evolu Process

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In the era of information technology, businesses have changed the way they work.Operations, which have become digitalized and managed via software systems, have increased in competitiveness. Software developers have been forced to design and implement their quality standards products by focusing on process-oriented management. Software quality aims to gain a high-performance level in the both development and managerial processes to maintain continuous improvements. Continuous improvements require more efficient process monitoring methods. Process mining, which provides recent techniques related to process monitoring, can be utilized with event log files to track the process performance. This research will extract the process mapping and performance statistics of the bug-fixing process from event logs to develop an efficient process monitoring system. This process will uncover bottlenecks causing inefficiencies and risks. The benefits of process mining techniques will also be discussed to highlight the implications. In this context, this article will present a case study on monitoring the bug-fixing process of a software product via process mining.It will reveal significant knowledge about subsequent steps and roles. KEYWORDS bug fixing, business process management, process mining, software quality 1 | INTRODUCTION During the fourth industrial revolution, autonomous systems, intelligent machines, and intelligent production lines were viewed as forefront concepts in all sectors. This required various software products and technologies. To keep a competitive pace with the changing market, companies needed to invest in information and communication technologies to benefit from software services. These attempts aimed to increase the value of quality and standardization in software. In some instances, this caused structural changes to software quality and standards.Software quality is a difficult and subjective term to describe. Defining a generic quality metric accepted by all software is also problematic.Therefore, the quality of software is defined as the degree of proficiency in responding to the user's software needs. It is considered the most basic measure of software success. 1The concept expressed by software is constantly changing and developing. Its rapid change has led to a variety of quests to manage the complexity of the software developer. These quests question the implementation of standards in the software development process, leading to breakthroughs for this sector-specific approach. 2 It is important to acknowledge the existence of quality metrics used to evaluate a software product's outcomes. Therefore, an approach is needed to obtain overall performance levels of a software system by aggregating performance indicators. 3,4 Software companies are investing in quality assurance to reduce software development, decrease maintenance costs, increase revenue, and expand profit margins. 5 The success of both products and manufacturers are threatened by costs associated with inadequate software and disappo...

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Automatic Real-Time Mining Software Process Activities From SVN Logs Using a Naive Bayes Classifier

Zhu

Dai

et al. 2019

IEEE Access

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The abundance of event data in current software configuration management systems makes it possible to discover software process models automatically by using actual observed behavior. However, traditional process mining algorithms cannot be applied to event logs recorded in software configuration management (SCM) systems, such as SVN, because of missing activity attributes. To address this problem, a software process activity classifier is proposed to build event-activity mapping relationships from software development event streams, revealing activity attributes and associating the activity to the original SVN log. The proposed approach extracts activity from the SVN log based on semantic features and introduces a novel technique based on a naive Bayes approach to associate event activities dynamically. The approach has been applied to two real-world software development process logs, ArgoUML and jEdit, consisting of more than 80,000 events, covering development information from 1998 to 2015. With the application of our approach to such data, activities can be extracted from event logs and a classifier can be constructed for adding activity attributes to new events. The results of the classification are evaluated in terms of precision rate, recall rate, and the F-measure. Overall, two real-world software development process logs are used to validate the method, and the experimental results show that the approach can mine software process activities from SVN log events automatically and in real-time.INDEX TERMS Activity classifier, machine learning, software process activity, SVN log.

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An Integrated Infrastructure Using Process Mining Techniques for Software Process Verification

Cited by 3 publications

References 15 publications

Data interoperability assessment, case of messaging‐based data exchanges

Data interoperability assessment, case of messaging‐based data exchanges

Monitoring the software bug‐fixing process through the process mining approach

Automatic Real-Time Mining Software Process Activities From SVN Logs Using a Naive Bayes Classifier

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