Software process line discovery

Blum, Fabian Rojas; Simmonds, Jocelyn; Bastarrica, María Cecilia

doi:10.1145/2785592.2785605

Cited by 5 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The higher the frequency, the greater the model generalization. For simplicity, the inverse arc degree of a Petri net, as described by Rojas [16], is used. This measure is based on the Petri net's place and transition degrees, i.e., the sum of the number of input and output arcs of places and transitions.…”

Section: Process Model Quality Criteriamentioning

confidence: 99%

X-Processes: Discovering More Accurate Business Process Models with a Genetic Algorithms Method

Fantinato

Peres

Reijers

2021

2021 IEEE 25th International Enterprise Distributed Object Computing Conference (EDOC)

View full text Add to dashboard Cite

Although process model discovery has been extensively investigated over the past two decades, existing discovery methods are still not considered fully satisfactory. One problem is the difficulty of discovering accurate process models, achievable with both high recall (or fitness) and high precision, particularly for real-world event logs. This paper introduces a process discovery method, namely X-Processes, based on genetic algorithms, which aims to optimize accuracy through the F-Score calculated between recall and precision. Although genetic algorithms have been used to discover process models, such methods also have limitations as do other non-genetic algorithms-based methods. Experimental results for 12 real-world event logs show the accuracy of the process models discovered by X-Processes is higher than those of six other state-of-the-art discovery methods, including one also based on genetic algorithms. Besides accuracy, X-Processes delivers sound process models. Although its execution time is longer than the other compared discovery methods, X-Processes emerges as a solution when the need for a highly accurate process model outweighs the hunger for agility.

show abstract

Section: Process Model Quality Criteriamentioning

confidence: 99%

X-Processes: Discovering More Accurate Business Process Models with a Genetic Algorithms Method

Fantinato

Peres

Reijers

2021

2021 IEEE 25th International Enterprise Distributed Object Computing Conference (EDOC)

View full text Add to dashboard Cite

show abstract

“…Among the many high‐quality papers selected for presentation at the conference, reviewers selected the following six papers for inclusion in this special issue: Piloting MDevSPICE®: The Medical Device Software Process Assessment Framework, Development and Validation of the MedITNet Assessment Framework: Improving Risk Management of Medical IT Networks, Integrated Systems Engineering and Software Requirements Engineering for Technical Systems, (Winner of the ICSSP Best Paper Award 2015) Tailoring Process Requirements for Software Product Assurance, (Winner of the ICSSP Best Short Paper Award 2015) Software Process Line Discovery, Towards a Change‐aware Process Environment for System and Software Process, …”

Section: International Conference On Software and Systems Process 2015mentioning

confidence: 99%

ICSSP 2015–Special Issue Introduction

Pfahl

Kuhrmann

Bendraou

et al. 2016

J Software Evolu Process

View full text Add to dashboard Cite

The v‐algorithm for discovering software process lines

Blum

Simmonds

Bastarrica

2016

J Software Evolu Process

Self Cite

View full text Add to dashboard Cite

A software company can define a software process line (SPrL) to deal with projects with different characteristics. This entails defining a base process and its variation points; the SPrL is then tailored to each project. This approach avoids the co-evolution problems but is expensive to set up. In companies that register project events, this information could be used to discover the SPrL. However, traditional discovery algorithms focus on extracting a single process, which can be overly complex and would not be useful for managing future projects. Filtering out less frequent behavior leads to the discovery of simpler models, but these may not include relevant behavior. To address these issues, we propose the v-algorithm, which discovers a SPrL from process logs. Two thresholds split the log into three clusters based on relation frequency. The first one is used to generate the base process, the second one is used to identify variable elements, and the last one is discarded. We used the v-algorithm to discover the SPrL of Mobius, a small Chilean software company. We also discuss how the values of the thresholds affect the process discovery quality dimensions, extending existing metrics to the SPrL case.

show abstract

Software process line discovery

Cited by 5 publications

References 24 publications

X-Processes: Discovering More Accurate Business Process Models with a Genetic Algorithms Method

X-Processes: Discovering More Accurate Business Process Models with a Genetic Algorithms Method

ICSSP 2015–Special Issue Introduction

The v‐algorithm for discovering software process lines

Contact Info

Product

Resources

About