Identifying Deviations in Software Processes
Author(s) -
Behjat Zuhaira,
Naveed Ahmad,
Tanzila Saba,
Junaid Haseeb,
Saif Ur Rehman Malik,
Umar Manzoor,
Muhammad A. Balubaid,
Adeel Anjum
Publication year - 2017
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2017.2757954
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Software process improvement and business process reengineering are concomitant for software companies that struggle to mature their processes to reduce software project failures. Process gap analysis is an indispensable activity of both the initiatives. It is the identification of deviations in any process from a standard well-defined process. To identify deviations, an as-is process (descriptive/current process) and its corresponding to-be process (prescriptive/standard) are required. However, there is a lack of reengineering tools that support automated gap analysis. Companies rely on manual identification of deviations. The literature discusses various graph matching algorithms/techniques that determine similarities and differences between two graphs. They can be used in software industry as well to achieve multiple objectives, such as process improvement. As these techniques present certain limitations, such as insufficient element coverage for process gap analysis, they cannot deal with process gap analysis per se. However, they establish a ground for a much sophisticated solution. This paper presents an improved gap analysis algorithm to identify deviations in processes. The proposed algorithm is formally verified and also evaluated using an example process model.
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