Software Process Analysis Methodology – A Methodology Based on Lessons Learned in Embracing Legacy Software

Leemans, M Maikel; Aalst, Wil M. P. van der; Brand, Mark van den; Schiffelers, R.R.H.; Lensink, Leonard

doi:10.1109/icsme.2018.00076

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…Authors constructed models of client-server communication with both sides clearly separable. Leemans et al proposed [21] and later developed [22] a methodology to analyse event logs of software systems. The goal is to reverse engineer a (possibly, legacy) system to reveal and grasp its behavioural characteristics.…”

Section: Related Workmentioning

confidence: 99%

Using Process Mining to Leverage the Development of a Family of Mobile Applications

Rezunik,

Perevoznikova,

Eremina

et al. 2023

Proceedings of ISP RAS

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Enterprises often provide their services via a family of applications based on various platforms. Applications in such a family can behave differently. Their development processes can differ as well. Moreover, modern development processes are often complex and sometimes vague. This can lead to bugs, defects, and unwanted discrepancies in applications. In this paper, we show that process mining can be applied to leverage the development in such a case. Real-life models can be discovered and investigated by the developer teams in order to reveal differences in application behaviour, find bugs, and highlight inefficiencies. We consider datasets with event data of two types. Firstly, we analyse event logs of Android and iOS applications of the same product family. Secondly, we consider event data from working repositories of these applications. We show how by analysing such datasets, the real-life development process can be discovered. Besides, application event logs can help to find more and less severe bugs and unwanted behaviour.

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Section: Related Workmentioning

confidence: 99%

Using Process Mining to Leverage the Development of a Family of Mobile Applications

Rezunik,

Perevoznikova,

Eremina

et al. 2023

Proceedings of ISP RAS

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“…On the other hand, the idea of automatically inferring state-based behavior from event logs (also known as passive learning) is not new. This topic has been extensively studied by the two communities of finite state machine inference [11] and process mining [1,29].…”

Section: Introductionmentioning

confidence: 99%

Reverse engineering models of software interfaces

Bera¹,

Schuts

Hooman³

et al. 2021

COMSIS J

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Cyber-physical systems consist of many hardware and software components. Over the lifetime of these systems their components are often replaced or updated. To avoid integration problems, formal specifications of component interface behavior are crucial. Such a formal specification captures not only the set of provided operations but also the order of using them and the constraints on their timing behavior. Usually the order of operations are expressed in terms of a state machine. For new components such a formal specification can be derived from requirements. However, for legacy components such interface descriptions are usually not available. So they have to be reverse engineered from existing event logs and source code. This costs a lot of time and does not scale very well. To improve the efficiency of this process, we present a passive learning technique for interface models inspired by process mining techniques. The approach is based on representing causal relations between events present in an event log and their timing information as a timed-causal graph. The graph is further processed and eventually transformed into a state machine and a set of timing constraints. Compared to other approaches in literature which focus on the general problem of inferring state-based behavior, we exploit patterns of client-server interactions in event logs.

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Progressing from Process Mining Insights to Process Improvement: Challenges and Recommendations

Stein Dani,

Leopold,

van der Werf

et al. 2023

Lecture Notes in Computer Science

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Software Process Analysis Methodology – A Methodology Based on Lessons Learned in Embracing Legacy Software

Cited by 9 publications

References 17 publications

Using Process Mining to Leverage the Development of a Family of Mobile Applications

Using Process Mining to Leverage the Development of a Family of Mobile Applications

Reverse engineering models of software interfaces

Progressing from Process Mining Insights to Process Improvement: Challenges and Recommendations

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