Predict Maintenance Effort Research Articles

Simulating Software Evolution to Evaluate the Reliability of Early Decision-making among Design Alternatives toward Maintainability

Critical decisions among design altern seventh atives with regards to maintainability arise early in the software design cycle. Existing comparison models relayed on the structural evolution of the used design patterns are suitable to support such decisions. However, their effectiveness on predicting maintenance effort is usually verified on a limited number of case studies under heterogeneous metrics. In this article, a multi-variable simulation model for validating the decision-making reliability of the derived formal comparison models for the significant designing problem of recursive hierarchies of part-whole aggregations, proposed in our prior work, is introduced. In the absence of a strict validation, the simulation model has been thoroughly calibrated concerning its decision-making precision based on empirical distributions from time-series analysis, approximating the highly uncertain nature of actual maintenance process. The decision reliability of the formal models has been statistically validated on a sample of 1,000 instances of design attributes representing the entire design space of the problem. Despite the limited accuracy of measurements, the results show that the models demonstrate an increasing reliability in a long-term perspective, even under assumptions of high variability. Thus, the modeling theory discussed in our prior work delivers reliable models that significantly reduce decision-risk and relevant maintenance cost.

Predicting Software Maintenance Effort by Mining Software Project Reports Using Inter-Version Validation

Changes in the software are unavoidable due to an ever changing dynamic and active environment wherein expectations and requirements of the users tend to change rapidly. As a result, software needs to upgrade itself from its previous version to the next version in order to meet expectations of the user. The upgradation of the software is in terms of total number of Lines of Code (LOC) that might have been inserted, deleted or modified in moving from one version of software to the next. These changes are maintained in the change reports which constitute of the defect ID and defect description. Defect description describes the cause of defect which might have occurred in the previous version of the software due to which either new LOC needs to be inserted or existing LOC need to be deleted or modified. A lot of effort is required to correct the defects identified in software at the maintenance phase i.e., when software is delivered at the customers end. Thus, in this paper, we intend to predict maintenance effort by analyzing the defect reports using text mining techniques and thereafter developing the prediction models using suitable machine learning algorithms viz. Multi-Layer Perceptron (MLP), Radial-Basis Function (RBF) network and Decision Tree (DT). We have considered the changes between three successive versions of ‘MMS’ application package of Android operating system and have performed inter-version validation where the model predicted using the version ‘v’ is validated on the subsequent version i.e., ‘v+1’. The performance of the model was evaluated using Receiver Operating Characteristics (ROC) analysis. The results indicated that the model predicted on ‘MMS’ 4.0 version using MLP algorithm has shown good results when validated on ‘MMS’ 4.1 version. On the other hand, the performance of RBF and DT algorithms has been consistently average in predicting the maintenance effort.

Assessing the capability of internal metrics as early indicators of maintenance effort through experimentation: Research Articles

The complexity of software artifacts is widely believed to be an important determinant of maintenance effort. This paper conducts an experimental analysis of the impact of complexity on the maintenance of the Unified Modeling Language (UML) class diagrams. This represents an analysis of the effect of an internal quality attribute on an external quality attribute. A range of complexity metrics are proposed based on an ontological analysis of the UML language and previous research. The relative influence of these metrics on maintenance effort is then evaluated using a laboratory experiment. A within-subjects design was used, with subjects required to modify a range of UML class diagrams with different levels of complexity. Only two of the metrics emerged as significant determinants of maintenance effort: number of methods and number of associations. Together these explain around 28p of the variation in maintenance effort. While these findings are encouraging, further research is necessary to explore the ability of these metrics to predict maintenance effort. Copyright © 2005 John Wiley & Sons, Ltd.

Assessing the capability of internal metrics as early indicators of maintenance effort through experimentation

The complexity of software artifacts is widely believed to be an important determinant of maintenance effort. This paper conducts an experimental analysis of the impact of complexity on the maintenance of the Unified Modeling Language (UML) class diagrams. This represents an analysis of the effect of an internal quality attribute on an external quality attribute. A range of complexity metrics are proposed based on an ontological analysis of the UML language and previous research. The relative influence of these metrics on maintenance effort is then evaluated using a laboratory experiment. A within-subjects design was used, with subjects required to modify a range of UML class diagrams with different levels of complexity. Only two of the metrics emerged as significant determinants of maintenance effort: number of methods and number of associations. Together these explain around 28% of the variation in maintenance effort. While these findings are encouraging, further research is necessary to explore the ability of these metrics to predict maintenance effort. Copyright © 2005 John Wiley & Sons, Ltd.

Complexity measures for rule-based programs

Software complexity measures are quantitative estimates of the amount of effort required by a programmer to comprehend a piece of code. Many measures have been designed for standard procedural languages, but little work has been done to apply software complexity concepts to nontraditional programming paradigms. This paper presents a collection of software complexity measures that were specifically designed to quantify the conceptual complexity of rule-based programs. These measures are divided into two classes: bulk measures, which estimate complexity by examining aspects of program size, and rule measures, which gauge complexity based on the ways in which program rules interact with data and other rules. A pilot study was conducted to assess the effectiveness of these measures. Several measures were found to correlate well with the study participants' ratings of program difficulty and the time required by them to answer questions that required comprehension of program elements. The physical order of program rules was also shown to affect comprehension. The authors conclude that the development of software complexity measures for particular programming paradigms may lead to better tools for managing program development and predicting maintenance effort in nontraditional programming environments.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>