- author = "Megha Ummat",
- title = "Design and Development of Models for Analyzing Software Evolution",
- school = "Delhi Technological University",
- year = "2019",
- address = "110 042, India",
- month = "14 " # may,
- keywords = "genetic algorithms, genetic programming, Gene Expression Programming, GFS-GP, SBSE, PSO",
-
URL = "
http://dspace.dtu.ac.in:8080/jspui/handle/repository/16678",
-
URL = "
http://dspace.dtu.ac.in:8080/jspui/bitstream/repository/16678/1/2k13phdco05_Megha_Thesis_may2019.pdf",
- size = "514 pages",
-
abstract = "Software systems are important business assets of any
organization. However, in order to maintain the value
of these assets, software evolution i.e. the process of
planning and implementing change to the existing
software systems is a crucial activity. One of the
prime concerns while implementing changes is to
maintain the quality of the software product as there
are fewer resources and rigid deadlines, which may
result in poor processes and software quality
degradation. In such a scenario, the responsibility of
a software practitioner is to envisage methods which
provide good quality software with ideal resource usage
at optimum costs. One such cost effective approach is
to develop models for predicting change-prone parts of
a software as these parts are considered as sources of
changes and defects in a software. Detection of
change-prone parts in the initial stages of software
development lifecycle will help software developers in
outlining competent resource usage during maintenance
activities, planning remedial actions for software
restructuring and implementing corrective actions for
early removal of software defects. Prediction of
change-prone parts in an object-oriented software
involves the use of various object-oriented metrics as
predictor variables, which are representative of
software characteristics such as size, coupling,
cohesion and inheritance. Furthermore, we need a
classification technique for developing an efficient
prediction model which is able to distinguish between
change-prone and not change-prone parts of a software.
The various elements involved in the creation of
software change prediction models need to be assessed
and improved to yield efficient change-prediction
models.
This thesis verifies and validates the relationship between several object-oriented metrics and change-proneness attribute of an object-oriented class to develop effective prediction models. We also analyse the trends of object-oriented metrics in an evolving software in order to ascertain how the structural characteristics of a software change with its evolution. The thesis also evaluates the use of a specific set of process metrics, which are named as evolution-based metrics. These metrics encapsulate the evolution history of a class in an object-oriented software. Furthermore, the effectiveness of a combined set of object-oriented metrics and evolution-based metrics have also been investigated for determining the change-prone nature of a class in an object-oriented software.
Apart from predictor variables, the thesis also evaluates several categories of data analysis techniques, which can be used for developing software change prediction models. The investigated categories include statistical techniques and machine learning techniques, which have been used by several researchers in this domain. However, a new class of techniques i.e. search-based algorithms and their hybridized versions have recently gained popularity. We first review the capabilities, advantages and the experimental set-ups required to use this set of algorithms. Furthermore, we explore their capability for developing models which determine the change-prone nature of a class. The thesis also proposes a new set of classification algorithms based on ensemble methodology, using a search-based algorithm as a base-classifier. The proposed algorithms produce outputs by aggregating a number of constituent classifiers, which are fitness variants of the same base-classifier namely Constricted Particle Swarm Optimization. We also propose a unique classifier, which outputs the best classifier amongst an ensemble of classifiers for each data point (object-oriented class).
The thesis also evaluates the scenario when the historical data used for developing a change prediction model is imbalanced in nature. A dataset is said to be of imbalanced nature, when the ratio of category of classes (change-prone and not change-prone) is disproportionate. In general, as the number of change-prone classes is few as compared to the number of not change-prone classes, effective learning is problematic. This is because the learning algorithm is provided with very few instances of change-prone classes, therefore, it is unable to learn their characteristics properly resulting in lower accuracy while determining change-prone classes. The thesis investigates the use of sampling methods and MetaCost learners for developing efficient change prediction models from imbalanced training data.
Apart from determining the change-prone nature of classes, it is also important to determine the impact of change in a software. We determine the change-impact of bug correction in a software i.e. the number of classes that would be affected when a specific software bug is corrected. Additionally, the thesis also proposes a categorization of software bugs into different levels on the basis of maintenance effort and change impact values in order to optimize maintenance resources.",
-
notes = "Software Change Prediction: A Systematic
Review
Software Bug Categorization using Change Impact and Maintenance Effort
TD-4470;
student Roll No.: 2k13/Ph.D./COE/05, Number DTU/Results/PhD./2019/168 2K13/PHDCO/05
nee Megha Khanna
Supervisor: Ruchika Malhotra",
