Towards Retrograde Process Analysis in Running Legacy Applications [original]

Towards Retrograde Process Analysis in Running

Legacy Applications

Marius Breitmayer, Lisa Arnold and Manfred Reichert

Institute of Databases and Information Systems, Ulm University, Germany

{marius.breitmayer,lisa.arnold,manfred.reichert}@uni-ulm.de

Abstract. Process mining algorithms are highly dependent on the exis-

tence and quality of event logs. In many cases, however, software systems

(e.g., legacy systems) do not leverage workflow engines capable of pro-

ducing high-quality event logs for process mining algorithms. As a result,

the application of process mining algorithms is drastically hampered for

such legacy systems. The generation of suitable event data from run-

ning legacy software systems, therefore, would foster approaches such as

process mining, data-based process documentation, and process-oriented

software migration of legacy systems. This paper discusses the need for

dedicated event log generation approaches in this context.

Keywords: legacy systems, process mining, code analysis, event log

1 Introduction

Software applications are implemented to address the needs of users, use cases,

and business processes. However, the majority of common software systems (e.g.,

legacy systems or individual software solutions) have not been designed with the

goal to provide high-quality process-related event logs that allow for compre-

hensive process analyses and visualizations with modern process mining tools.

Relevant questions emerging in legacy software modernization projects include,

for example, how the process implemented by the legacy software system is

structured (Process Discovery) or to what extent its execution deviates from a

predefined to-be process (Conformance Checking). Currently, there exist three

basic approaches to obtain process models:

1. Log analysis uses existing logs (e.g., event logs) to reconstruct the imple-

mented process based on audit or workflow data. Consequently, the quality

of the resulting process model is directly correlated with both the existence

and quality of corresponding event logs [2,3]. However, a vast majority of

individual applications and legacy systems are often unable to provide ap-

propriate event logs. Moreover, even database-centric applications typically

do not provide transaction-level audit data. Consequently, there has been no

effective entry point for process mining yet.

2. Interviews may be conducted to discover the desired process model as

perceived by key users and process owners [9]. Additionally, data models

may be parsed to identify effects of processes on corresponding data. Ana-

lyzing such data models enables assumptions on the underlying processes.

This approach, however, is very time consuming and paved with both mis-

understandings and misconceptions. In addition, interviews do not ensure

completeness of the relevant processes and their various aspects, as they

often neglect exceptions or specific process perspectives (e.g., data, time).

3. Pattern recognition attempts to identify typical process patterns in var-

ious data pools using algorithms from the field of artificial intelligence [1].

The algorithms require a deep analysis and learning phase prior to their

application to the raw data. This is a time-consuming, cost-intensive, and

fuzzy approach, which is therefore hardly pursued.

In the context of legacy systems, however, none of the presented approaches is

easily applicable. All three approaches have in common that the business pro-

cesses (and event logs), implemented by the legacy software systems, need to be

represented accurately. Since most individual software solutions do not neces-

sarily use process engines capable of delivering suitable process data, alternative

approaches are required. One approach to tackle this challenge is, to observe

process participants during process execution and to record their interactions

with the software system resulting in a fine-grained documentation.

Section 2 describes the proposed solution approach. Section 3 discusses re-

lated work. Finally, Section 4 provides a summary and outlook.

2 Solution Approach

A human-centered business process can be defined as a sequence of user interac-

tions with a software application, where each interaction is subject-bound (i.e.,

part of the same transaction). In legacy systems, such processes can be initiated

and terminated by suitable actions (e.g., pre-defined key combinations or menu

items). Adding such actions to an event stream with the associated application

object (e.g., an order identified by its unique order number), subsequently, pro-

cess mining tools will have process related event logs as input. The collected

event data may then constitute the basis for a plethora of use cases, such as

process documentation, process mining, and process-oriented cost estimations

for modernizing legacy software systems (i.e., software migration). We aim to

create different logging variants for existing legacy production systems:

1. Dedicated recording documents existing processes by assigning related

program components. Users may determine the start and end of the recording

using predefined key combinations, thus precisely delimiting all activities

that constitute the recorded process (or the considered process part).

2. Silent recording tracks the entire usage of the application from the first

this should be done for all sessions or only for selected user sessions (e.g.,

only sessions of users from a certain department). Furthermore, it may be

configured, which information should be stored (e.g., to ensure compliance

with data protection requirements).

To minimize the performance effects of these recording on running applications,

we rely on existing logging mechanisms of the application infrastructure.

For Oracle applications using a WebLogic Server, for example, Oracle Diag-

nostic Logging (ODL) offers extensive possibilities to manage application infor-

mation via the administration console. Among others, oracle logger classes (e.g.,

Application Development Framework) may use this information through ODL

handlers [15]. In Single Page Applications (e.g., the Oracle JavaScript Extension

Toolkit JET), the primary object is known, however, the context between mul-

tiple process steps may get lost due to the loose coupling of user sessions and

services. Even applications based on Oracles Forms allow adding appropriate

message calls for each PL/SQL unit.

Using existing system logging functionality, the recording quality is signifi-

cantly increased compared to purely mining the data model, as user interactions

can be unambiguously linked to the process, program code, and associated data.

Fig. 1 depicts the approach. In a first step we identify relevant objects using

information from the database and the source code of the application. However,

especially in databases of legacy systems, assumptions such as good normaliza-

tion or even the existence of foreign key constraints are often not applicable.

The reason for this is that in many cases the logic is represented in the source

code of applications rather than the database. By combining knowledge from

the database (e.g., create, read, update, and delete -operations) and correspond-

ing source code (e.g., code fragments corresponding to such operations), we are

able to tackle this issue. After having identified process-relevant objects in both

source code and database, we correlate them and add code tracking capabilities

to the legacy system using, for example, the possibilities mentioned previously.

This does then enable the generation of event logs from either dedicated or silent

recording. These event logs may then be used during analysis.

Real-Time

Production

System

Legacy

System

Process

Real-Time

Data

(e.g. Event

Stream, XES)

Data

Cube

Data Synchonization

Event Stream

Repository

Code Tracker ( Pre-installation step )

API

Parser

AST

(Abstract

Syntax Tree)

Source code

• Programming

languages

• Scripting

languages

Database

• Schema

• Instances

• Distribution

DWH

ASCII Files

Process Visualization

Meta Data

Fig. 1. General approach

When analyzing event logs generated from such legacy systems, a valuable

effect can be achieved that the three approaches described in Section 1 are unable

to provide: If certain entries in the event stream are missing when comparing the

event stream with the source code, this indicates that the process steps involved,

although implemented and present, have never been used. This information is

essential when removing technical debts and modernizing legacy systems [8].

3 Related Work

This work is related to the research areas process mining, event log generation,

and code analysis. Process mining [2] provides techniques to discover business

process models from event logs [16,12], to evaluate conformance between process

event logs and models [6], and to enhance processes [3]. Existing process dis-

covery approaches mainly focus on the control flow perspective while the data

perspective is mostly neglected [13]. The latter is of particular interest for mean-

ingful process analysis and improvements (e.g., legacy system migration to new

software architectures).

Event log generation is concerned with the generation of event log based on

various sources. In [11,4], approaches to record user activities based on desktop

actions (e.g., for robotic process automation) are presented. Our approach is

also able to correlate such desktop actions with the corresponding source code

fragments and database operations, allowing for a more detailed event log gen-

eration. The case study presented in [14] discusses the generation of event logs

from a real-world data warehouse of a large U.S. health system. While some

challenges (e.g., correlating events) may also arise in the context of legacy sys-

tems, we plan to minimize required domain expert interviews by automatically

extracting domain knowledge from the source code.

Code analysis comprises traditional analysis (e.g., style checking or data flow

analysis [10]) and profiling (e.g., CEGAR [7] and BMC [5]) which, combined with

process knowledge, yield great potential for software improvement and migration.

4 Conclusion and Outlook

This paper emphasizes the need for spending research efforts on the recording

of high quality event data in legacy systems. This not only enables the appli-

cation of existing process mining algorithms, but also additional use cases such

as, for example, data-driven process documentation, facilitation software migra-

tion projects or cost reduction through process-driven development. Note that

corresponding work is also relevant in the context of robotic process automation

[17].

Acknowledgments This work is part of the SoftProc project, funded by the

KMU Innovativ Program of the Federal Ministry of Education and Research,

Germany (F.No. 01IS20027A)

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