Mapping Process Mining Techniques to Agile Software Development

Perspectives

Cyrine Feres

, Sonia Ayachi Ghannouchi

and Ricardo Martinho

High Institute of Management of Sousse, University of Sousse, Tunisia

INESCC - DL, ESTG, Polytechnic of Leiria, Portugal

Keywords: Process Mining, Agile Software Development Processes, Mapping.

Abstract: Agile Software Development (ASD) processes have surfaced as an effective alternative for more efficient

software project management. They concentrate on a set of informal best practices instead of a standardised

process, making it difficult to determine the degree of real implementation in an organization. Process Mining

(PM) can play a key role in such analysis by discovering the software development process model followed

in a certain set of software projects, and by analysing event logs that report the projects’ executed tasks. These

discovered processes can then be compared to standardised ASD methods such as Scrum and eXtreme

Programming (XP), and improved accordingly. Motivated by this, we present in this paper a literature review

revealing the state of the art of Process Mining and its usage in ASD processes, but under a correlation between

the three main research areas of PM (discovery, conformance, and enhancement), and the main ASD process

perspectives including organisational/team, control-flow, quality, time, cost & risk, and data. We then analyse

and discuss the results of this review quantitatively and qualitatively and prospect future opportunities for

research accordingly.

1 INTRODUCTION

Over the last two decades, software engineers have

been constantly looking for better ways to create

high-quality software in a timely manner. The

popularity of Agile Software Development (ASD)

processes, combined with collaboration tools, has

emerged as a flexible solution to these challenges

(Erdem and Demirörs, 2017; Erdem et al, 2018).

As with other kinds of business processes, ASD

processes can be analysed from several process

perspectives. For instance, the time perspective in the

Scrum framework is reflected by sprints with a fixed

duration, daily scrum meetings, effort estimations set

in sprint planning meetings and sprint reviews and

retrospectives duration and scheduling. The

resources perspective mainly includes Developers,

the Scrum Master and the Product Owner, as well as

their assignments to tasks. The control-flow

perspective can be reflected by the sequence of

activities performed for a certain software project. In

https://orcid.org/0000-0002-6645-8490

https://orcid.org/0000-0001-9583-9797

https://orcid.org/0000-0003-1157-7510

a Scrum scenario, this could mean starting with

prioritizing the user stories in the product backlog,

picking them to the sprint backlog, developing them

and showing them to the stakeholders, while

collecting feedback and improvements for the next

sprint.

Process Mining (PM) has been used successfully

in a variety of fields, including software engineering

and, particularly, ASD processes (Urrea-Contreras et

al, 2021). In this case, PM is based on event logs that

can be collected from software project management

information systems and/or other data sources, and

may include the project ID, the tasks executed, their

corresponding user stories, the assigned team

members, and their start and end timestamps, among

other data. From here, PM techniques can address

three major purposes: discovery, conformance

checking and enhancement of ASD processes. Taking

Scrum as an example, PM discovery techniques can

be used to identify important metrics such as mean

number of sprints per user story, most efficient

428

Feres, C., Ghannouchi, S. and Martinho, R.

Mapping Process Mining Techniques to Agile Software Development Perspectives.

DOI: 10.5220/0011850500003464

In Proceedings of the 18th Inter national Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2023), pages 428-435

ISBN: 978-989-758-647-7; ISSN: 2184-4895

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

developer per type of user story, or simply the most

common sequence of tasks performed to develop and

finish a user story. Conformance checking can elicit

deviations against a standard Scrum sequence of

events, and enhancement can be reflected by

adjusting developer assignments to certain types of

tasks, or even to help estimate user story points.

We can find in literature systematic reviews and

mappings on the usage of PM in ASD processes, such

as the ones presented in Erdem and Demirörs (2017),

Erdem et al (2018) and Arias et al (2018).

Nevertheless, it is not obvious to understand, from

these contributions, which ASD process perspectives

have been under analyses with PM, particularly for

which purpose. In this paper we perform a literature

review on the use of PM in ASD processes, but in the

form of a mapping between PM techniques and ASD

process perspectives. In this way, we intent to provide

an insight on how research has addressed the

correlation between these two fields. This means that

we’ll be matching discovery, conformance and

enhancement PM techniques with commonly

addressed ASD process perspectives such as

organisational/team, control-flow, quality, time, cost,

risk, and data.

We provide insight into how such results were

obtained by using the PRISMA method (Page et al,

2021) which consists in a set of recommended

activities on a flow diagram, towards the

development of a systematic review. Next, we present

the main results under a matrix-based heat map

(cross-reference between PM techniques and ASD

process perspectives). We then analyse these

correlations from both quantitative and qualitative

points of view. Our goal is to identify the most and

least studied correlations, as well as their specific

applications and goals, in order to uncover research

opportunities in these fields.

The paper is structured as follows: brief

overviews of ASD processes and PM are given in

section 2. The research method is presented in section

3, and our matrix-based analysis is illustrated in

section 4, including quantitative and qualitative

descriptions of the analysed research works. Finally,

in section 5 we discuss the results and present future

research perspectives on these themes.

2 BACKGROUND

In this section, we first provide an overview of ASD

processes and their different perspectives. Then we

highlight the main techniques used in PM and their

purpose.

2.1 Agile Software Development

Processes

Agile Software Development (ASD) processes are

becoming more popular in the information systems

field. Nowadays, a significant proportion of software

organizations develop software that uses ASD

processes (Garousi et al, 2015). ASD processes allow

project managers and workers to conform to

constantly changing contexts while not imposing

rigid control. ASD projects are typically established

by small teams over a shorter period of iterations

(Beck et al, 2001). Customers are frequently delivered

working software to test during the ASD process. As

a result, there is a high probability that changes will

occur throughout the development process, and can

even be decided at late stages of development to

ensure customer satisfaction (Marquez et al, 2018).

The Manifesto for ASD processes recommends

stakeholders to motivate agile teams, guarantees

coordination and communication among teams and

customers, and holds meetings to effectively and

efficiently transmit information between themselves

(Beck et al, 2001).

ASD processes can also be considered as a

conjunction of process elements from several

perspectives. For instance, the organizational/team

perspective can deal with team members and their

assignments to tasks.

The control-flow perspective is concerned in

identifying the tasks executed and their

corresponding sequences, while the time perspective

can refer to the duration of events such as tasks,

sprints and meetings. The quality perspective can

address the quality of a software release measured

through, for example, customer satisfaction

assessment. Other commonly referred perspectives

include cost, risk and data, which can be reflected in

business values assigned to user stories, prioritising

user stories that are more uncertain and under-

defined, and managing documentation of the software

product, respectively.

2.2 Process Mining

Process Mining (PM) emerged in the last decade with

several research studies that have been carried out,

and the trend is impressive. PM is a process

management research field that analyses business

processes using event logs as the starting point. It

must include a minimum of three elements that

describe the execution of activities: an identifier of

the case for a certain activity executed (for instance,

the software project to which it belongs), a label for

Mapping Process Mining Techniques to Agile Software Development Perspectives

429

the name of the activity and its (start time) timestamp

(Urrea-Contreras et al, 2021). Other information that

can be part of an event log includes resources used to

initiate or operate the activity (e.g.: operators/team

member(s), roles, materials, equipment), cost, risk,

quality, and data produced.

The aim of PM is to discover, monitor, and

improve real processes (rather than assumed

processes) by extracting data from readily available

event logs in today's information systems and data

sources (van der Aalst, 2016). These types of mining

can be focused on several process perspectives such

as time, control-flow, resources and case perspectives

(van der Aalst, 2016). PM is useful for several

reasons, namely (Van der Aalst, 2012):

• Provides insight into organisational business

processes;

• Enables the organisation's business processes

to be tested (whether processes are executed

according to the rules and within

boundaries);

• Helps in optimising and enhancing business

processes and performance;

• Allows for the evaluation and improved

decision-making for running cases in a

certain business process.

These business processes can be diverse in their

domain and evidently include ASD processes as well.

3 RESEARCH METHOD

In this section, we illustrate how we used the

PRISMA statement

method to perform a literature

review on the correlation of PM and ASD processes.

3.1 PRISMA Statement

Preferred Reporting Items for Systematic Reviews

and Meta-Analyses (PRISMA) (Page et al, 2021)

consists of a series of checks on a flow diagram (as

illustrated in Figure 1), considering essential items for

the development of a systematic review. Briefly, it

foresees the definition of search strings and the choice

of the research sources, the adoption of selection

criteria unfolding the results of the search, and then

the extraction of the different limitations of the

related research works. Figure 1 shows the summary

of results obtained from the application of the

PRISMA statement to our domains of knowledge.

https://www.prisma-statement.org

Regarding the databases considered for the

search, ten were selected as data sources to collect

and obtain the largest number of studies. These

included: Springer Link, IEEE Xplore, Google

Scholar, Science Direct, Web of Science, Base

Search, Research Gate, Scopus, Scinapse, and

Science.gov. For searching these databases, we

considered the following search string: “Process

mining” AND “Agile Software Development” OR

“Process mining” AND “Agile Software” OR

“Process mining” AND “Agile Software Lifecycle”

OR “Process mining” AND “Scrum” OR “Process

mining” AND “Extreme Programming” OR “Process

Mining Perspectives” AND “Agile Software

Development” OR “Process Mining” AND

“Software Engineering” OR “Process Mining” AND

“Business Process”. After collecting and counting the

results, we divided the study selection process into

two stages: 1) search results were evaluated by

reading the title and abstract. The main criterion in

this stage was the inclusion of words and context

regarding PM and ASD processes; and 2) the

remaining results were filtered, based on the

following inclusion criteria:

• Scope related to PM in the context of ASD;

• Full text is available;

• Written in English;

• Published between 2009 and 2022.

Before the selection process was completed, the

references of the selected papers were checked to find

related papers and to increase the size of the result set.

Information from all papers was gathered in the

form of an Excel file, resulting in 4444 papers + 4

duplicates (removed in the Screening section of

PRISMA). The resulting papers were firstly screened

based on the title (where we excluded 3315), and then

by keywords and abstract (excluded 1129 more)

resulting in 18 papers for a full-text reading. In the

next section we provide details on these final results,

considering their mapping onto ASD process

perspectives and PM techniques.

4 SYSTEMATIC MAPPING

In this section, we begin by describing and

exemplifying the benefits PM can bring to ASD

processes, then we analyse the final 18 results from

our PRISMA method.

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Figure 1: Literature review results found and filtered according to the PRISMA statement.

4.1 PM in ASD Processes

Process Mining offers tools for discovering agile

processes used by agile teams to clarify an

organization's reality, and can also be used to improve

the process quality during agile software

development processes (Erdem and Demirörs, 2017).

Currently, ASD processes/frameworks’ best

practices such as those represented by Scrum

Behavior Driven Development (BDD)

, or eXtreme

Programming (XP)

are commonly built into software

project life cycle management tools to assist teams in

carrying out their software projects. From here, event

logs on the execution of these projects can be

registered and later collected to be fed into Process

Mining algorithms. These can then output several

results, which include performance graphs illustrating

control-flow and time-related values, common teams

and their collaborations, or even frequency graphs to

account for rework, cost, risk, and other quality

attributes. As a side contribution of this paper, we

present in Figure 2 a concrete illustration of how results

from PM can help ASD processes. For this, we also

present the well-known Scrum framework, decorated

with some examples of results originated from the

application of PM techniques, as well as the decisions

they can support to enhance the Scrum ASD process.

In this Figure, the main Scrum concepts are associated

with examples of tuples in the following form:

https://scrumguides.org/

https://dannorth.net/introducing-bdd/

http://www.extremeprogramming.org/

Records identified from:

Databases (n = 10)

Registers (n = 4448)

Records removed before screening:

Duplicate records removed (n=4)

Records marked as ineligible by

automation tools (n =0)

Records removed for other reasons

(n =0)

Records screened

(n =4444)

Records excluded

(n =3315)

Reports sought for retrieval

(n =1129)

Reports not retrieved

(n =1111)

Reports assessed for eligibility

(n =18)

Reports excluded:

Reason 1 (n = Lack of information

about our research area)

Reason 2 (n = Articles are about

software repositories)

Studies included in review

(n =3)

Reports of included studies

(n =15)

Identification of studies via databases and registers

Identification

Screening

Include

Mapping Process Mining Techniques to Agile Software Development Perspectives

431

Figure 2: PM applied to the Scrum framework.

<Result obtained from PM>: <decision that can

be supported by this result>

For instance, for the Product Backlog, we can

measure the mean deviation between estimated effort

and real effort for certain types of User Stories (tuple

number 1 in Figure 2) so that we can estimate how

much effort should be considered for similar User

Stories. For tuple 2, we can obtain from PM the mean

business value for a certain type of User Stories, to

have a reference value for future ones.

For the Sprint Planning Meeting, we can consider

the waiting time between similar activities in past

projects (for instance, through a performance graph

from PM), as well as the best sequencing performance

between activities and best performance team for

certain activities (tuples 3, 4 and 5), so that we can

plan the next sprint accordingly.

For Daily Scrum Meetings (tuple 6), we can use,

for instance, Multi-perspective PM techniques

(Mannhardt et al, 2015) to retrieve the most efficient

schedule for daily meetings, based on past projects’

data (time of day and comparative amount of work

done further).

For the Product Increment, we can measure the

most cost-effective test policies, so that we can decide

which kind of tests we should favour (tuple 7). Also,

we can mine through PM the best performance

sequencing of product increments in similar software

projects, to better decide the next product increment

(tuple 8).

Tuples 9 and 10 are related to the Sprint review

event of Scrum. Similarly, we can measure the

number of user stories that surpassed one sprint till

concluded, to assess the most suitable sprint duration

for similar projects (tuple 9).

This can be done, for instance, by using the

Heuristics Miner from PM (Weijters et al, 2006),

considering the sprint ID as the case identifier, and

evaluating user stories that appear in more than one

sprint. We can also determine the best timing for

sprint reviews, based on this result, in conjunction

with data relative to the week day/time of each sprint

review event. This also applies in tuples 12 and 13 for

Sprint retrospective events, in order to derive the time

and kind of these meetings. Here, we can also use, for

instance, the Inductive Visual Miner algorithm

(Weijters et al, 2006) to analyse conformance and

deviations from a standard Scrum process, to infer the

changes in the process needed for a particular project

(tuple 11).

4.2 Quantitative Analysis

In Table 1, we present these 18 research works in the

form of a grey-shaded heat map, where darker cells

represent more papers addressing a certain mapping

of PM technique versus ASD process perspective. We

can conclude that most of these works are focused on

the organisational/team and control-flow

perspectives of ASD process, and with a higher

frequency on the application of PM discovery

techniques (10 and 14 research works, respectively).

The second most addressed correlations are those

related to the quality and time perspectives, again

with higher incidence on the application of discovery

PM techniques (8 and 4 works, respectively). Less

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referred correlations include the ones from the cost &

risk and data perspectives, with 2 research works per

correlation cell. In the following section, we provide

deeper details on the exact contributions for each of

these research works by PM techniques.

4.3 Qualitative Analysis

The most commonly application of PM to ASD is

process discovery, with the purpose of uncovering the

really accomplished processes in organisations.

Agile team activities can be collected to discover

what is going on and how it is going on. Process

discovery will be beneficial in extracting the steps

taken by agile teams, required inputs to improvement,

intermediate outputs developed within iterations, and

roles that have evolved during development. This is

advocated in the research works referred in the

discovery – organisational/team correlation from

Table 1. Caldeira and Abreu (2016) and Shani et al,

(2019) focus on discovering the time perspectives

which means the time spent on each activity, which

activity is causing a bottleneck, what activity is done

the most on a particular time, and if exists a particular

moment where activities are slower to get done. This

can indicate inefficiencies in the ASD process model,

particularly in the early stages of software

development. Activities that cause bottlenecks can be

identified immediately with the event log streamed to

the system. This will accelerate the entire

development process, allowing software products to

be delivered to customers more quickly.

Rubin et al. (2014b) discovered that the different

interpretations of agile method rules by teams in an

organization may result in interoperability issues

between the organisation's projects. The works of

Akman and Demirörs (2009) and Aalst (2015) focus

on discovering the control-flow of the software

process. This is achieved by analysing the order of

activities and highlighting the most frequent activity

paths in software development, as well as analysing

how each task/activity follows each other in an event

log and inferring a possible model for the behaviour

captured in the observed process.

Caldeira et al. (2019) and Fauzi and Andreswari

(2022) discuss that ASD perspectives such as

organisational/team performance, time, and control-

flow can reflect on the cost and risk of ASD

processes. Urrea-Contreras et al. (2021) advocate,

through a systematic literature review, that PM

techniques are important for software development

processes’ management. The results illustrate a

research gap in the successful execution of the case

and time process perspectives. The systematic

mapping study of Arias et al (2018) proves that PM

discovery techniques (namely, performance analysis)

were used in three different studies. To diagnose

performance issues, a process model is discovered,

and time information is annotated for this type of

analysis. Lemos et al. (2011) explore the

conformance checking of the formal software

development process defined by a company and

evaluate the application of PM for making such work

less costly and more effective. The authors focused at

the control-flow perspective in the presented

conformance analysis. Rubin et al. (2014a) also

correlate PM and ASD. This work describes a

bottom-up approach for analyzing user and system

runtime behaviour and improving software by using

event logs (e.g., trace data) from a software system.

They are concerned with development processes, but

focused on the enhancement of software functionality

through the use of PM techniques in an agile manner.

5 CONCLUSIONS

Motivated by the lack, to the best of our knowledge,

of a systematic mapping/literature review correlating

the two fields of ADS processes and PM, we used the

PRISMA statement to perform a literature review.

We identified 18 resulting research works, being the

majority published within the last decade.

In Figure 1 we illustrated through Scrum how PM

techniques can serve decision makers on the

improvement of ASD process. We then correlated

PM techniques with ASD process perspectives

through a heat map (Table 1), to provide a better

structured global analysis, as well as to identify most

and least addressed research themes. From here, we

can conclude that, within these research works, PM

techniques applied are mostly related with discovery

of the organisational/team and control-flow ASD

process perspectives. Also, we can observe a

significant difference between these correlations and

the ones concerning the cost & risk and data

perspectives. Even for the time perspective, less than

¼ of the research works check for the application of

any PM technique.

Mapping Process Mining Techniques to Agile Software Development Perspectives

433

Table 1: Correlation of Process Mining with Agile Software Development Process Perspectives.

Process

Mining

Techniques

Agile Software Development Process Perspectives

Organisational /Team Control-Flow Quality

Time

Cost

& Risk

Data

Discovery

Erdem and Demirörs

(2017), Marquez et

al. (2018), Caldeira et

al. (2019), Fauzi and

Andreswari (2022),

Rubin et al. (2014b),

Urrea-Contreras et al.

(2021), Caldeira and

e Abreu (2016),

Erdemet et al. (2018),

Zayed and Farid

(2016), Arias et al.

(2018)

Rubin et al. (2014a),

Erdem and Demirörs

(2017), Akman and

Demirörs (2009),

Marquez et al. (2018),

Caldeira et al. (2019),

Aalst (2015), Fauzi

and Andreswari (2022),

Rubin et al. (2014b),

Keithand Vega (2017),

Urrea-Contreras et al.

(2021),

Caldeira and e Abreu

(2016), Zayed and

Farid (2016), Arias et

al. (2018), Lemos et al.

(2011)

Rubin et al.

(2014a),

Erdem and

Demirörs

(2017),

Marquez et

al. (2018),

Caldeira et al.

(2019), Rubin

et al. (2014b),

Keithand

Vega (2017),

Shani et al.

(2019),

Lemos et al.

(2011)

Marquez

et al.

(2018),

Urrea-

Contreras

et al.

(2021),

Caldeira

and e

Abreu

(2016),

Shani et

al. (2019)

Caldeira et

al. (2019),

Fauzi and

Andreswari.

(2022)

Sebu and

Ciocarlie

(2014),

Zayed and

Farid

(2016)

Conformance

checking

Marquez et al.

(2018), Caldeira et al.

(2019), Fauzi and

Andreswari (2022),

Ardimento et al.

(2019), Caldeira and

e Abreu (2016),

Erdemet et al. (2018)

Rubin et al. (2014a),

Marquez et al. (2018),

Caldeira et al. (2019),

Aalst (2015), Fauzi and

Andreswari (2022),

Rubin et al. (2014b),

Caldeira and e Abreu

(2016), Lemos et al.

(2011)

Rubin et al.

(2014a),

Caldeira et al.

(2019), Rubin

et al. (2014b),

Shani et al.

(2019),

Lemos et al.

(2011)

Marquez

et al.

(2018),

Caldeira

and e

Abreu

(2016),

Shani et

al. (2019)

Caldeira et

al. (2019),

Fauzi and

Andreswari

(2022)

Sebu and

Ciocarlie

(2014),

Zayed

and Farid

(2016)

Enhancement

Marquez et al.

(2018), Fauzi and

Andreswari (2022),

Ardimento et al.

(2019), Rubin et al.

(2014b),

Caldeira and e Abreu

(2016), Erdemet et al.

(2018)

Rubin et al. (2014a),

Marquez et al. (2018),

Aalst (2015), Fauzi and

Andreswari (2022),

Rubin et al. (2014b),

Keithand Vega (2017),

Caldeira and e Abreu

(2016)

Rubin et al.

(2014a),

Rubin et al.

(2014b),

Keithand

Vega (2017),

Shani et al.

(2019)

Marquez

et al.

(2018),

Caldeira

and e

Abreu

(2016),

Shani et

al. (2019)

Fauzi and

Andreswari

(2022)

Sebu and

Ciocarlie

(2014)

Qualitatively, we can observe that the majority of

these research studies aim to demonstrate that PM can

be a useful tool to improve the implementation of

ASD process perspectives, namely:

• Organisational/team: Enhancing the

process model with organisational structures

that combine social network analysis,

mapping resource behaviours, user

collaboration, and role analysis;

• Control-flow: Defining a method for

examining how each task/activity follows

the next in an event log and deducing a

possible model for the behaviour recorded in

the observable process;

• Quality: Addressing software quality as one

of the main goals of ASD process to obtain

a high level of customer satisfaction, which

can be affected by time, team performance,

and cost of the project;

• Time: Concerning the frequency of events

and their timing and used them to predict

remaining and analysing the processing time

or duration of an activity;

• Cost & risk: Associating costs and risks

mainly to planning activities, which can also

be highly affected by scope, time, and

quality during the project;

• Data: representing all the information

consumed and produced during one release

of a software product in ASD process.

Taking these considerations into account, this

paper can be used as a reference and brief guide for

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future researchers working in the cross-field of PM

and ASD processes.

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