Software Maintainability Estimation in Agile Software Development

Agile methodologies have gained wide acceptance for developing high-quality products with a quick and flexible approach. However, until now, the quality of the agile process has not been validated quantitatively. Quality being important for the software system, there is a need for measurement. Estimating different quality factors will lead to a quality product. Also, agile software development does not provide any precise models to evaluate maintainability. Therefore, there is a need for an algorithmic approach that can serve as the basis for estimation of maintainability. The article proposes an adaptive neuro-fuzzy inference system (ANFIS) model for estimating agile maintainability. Maintainability is one of the prominent quality factors in the case of agile development. The proposed model has been verified and found to be effective for assessing the maintainability of agile software.

Software quality improvement and validation using reengineering

In software development life cycle, software maintenance is among the critical phases. It is a post-implementation activity that requires rigorous human efforts. For any software developer, maintaining software for a longer period is the primary objective. This objective can be accomplished if good quality software is developed. Maintainability is one of the vital characteristics of software maintenance. Maintainability enables developers to keep the system alive for a longer period of time at a limited cost. Software Maintainability can be enhanced using reengineering. The proposed research validates improvement in the quality of the reengineered software system. The quality of the software is analyzed using a coupling, cohesion, inheritance, and other essential design metrics. The observed improvement in the software design is 62.1%. The execution time of the software is also reduced by 6.5%. Reduction in the cost of maintenance is also another important outcome of this research. The observed reduction in the maintenance cost is 36.8%. Thus, the main objective of the proposed research is to analyze and validate the quality improvement in the reengineered software. Agile Scrum methodology has been used to perform software reengineering. Design Metrics are measured using the Chidamber and Kemerer Java metric (CKJM) version-9.0 tool. For reengineering implementation, Net Beans 7.3 has been used.

Python Code Smell Refactoring Route Generation Based on Association Rule and Correlation

Code smell is a software quality problem caused by software design flaws. Refactoring code smells can improve software maintainability. While prior works mostly focused on Java code smells, only a few prior researches detect and refactor code smells of Python. Therefore, we intend to outline a route (i.e. sequential refactoring operation) for refactoring Python code smells, including LC, LM, LMC, LPL, LSC, LBCL, LLF, MNC, CCC and LTCE. The route could instruct developers to save effort by refactoring the smell strongly correlated with other smells in advance. As a result, more smells could be resolved by a single refactoring. First, we reveal the co-occurrence and the inter-causation between smells. Then, we evaluate the smells’ correlation. Results highlight seven groups of smells with high co-occurrence. Meanwhile, 10 groups of smells correlate with each other in a significant level of Spearman’s correlation coefficient at 0.01. Finally, we generate the refactoring route based on the association rules, we exploit an empirical verification with 10 developers involved. The results of Kendall’s Tau show that the proposed refactoring route has a high inter-agreement with the developer’s perception. In conclusion, we propose four refactoring routes to provide guidance for practitioners, i.e. {LPL [Formula: see text] LLF}, {LPL [Formula: see text] LBCL}, {LPL [Formula: see text] LMC} and {LPL [Formula: see text] LM [Formula: see text] LC [Formula: see text] CCC [Formula: see text] MNC}.