Identifying Effort Estimation Factors for Corrective Maintenance in Object-Oriented Systems

This research explores the decision-making process of expert estimators of corrective maintenance projects by usingqualitative methods to identify the factors that they use in deriving estimates. We implement a technique called causalmapping, which allows us to identify the cognitive links between the information that estimators use, and the estimates thatthey produce based on that information. Results suggest that a total of 17 factors may be relevant for corrective maintenanceeffort estimation, covering constructs related to developers, code, defects, and environment. This line of research aims ataddressing the limitations of existing maintenance estimation models that do not incorporate a number of soft factors, thus,achieving less accurate estimates than human experts

Application of ensemble techniques in predicting object-oriented software maintainability

While prior object-oriented software maintainability literature acknowledges the role of machine learning techniques as valuable predictors of potential change, the most suitable technique that achieves consistently high accuracy remains undetermined. With the objective of obtaining more consistent results, an ensemble technique is investigated to advance the performance of the individual models and increase their accuracy in predicting software maintainability of the object-oriented system. This paper describes the research plan for predicting object-oriented software maintainability using ensemble techniques. First, we present a brief overview of the main research background and its different components. Second, we explain the research methodology. Third, we provide expected results. Finally, we conclude summary of the current status

Predicting software maintainability in object-oriented systems using ensemble techniques

Prediction of the maintainability of classes in object-oriented systems is a significant factor for software success, however it is a challenging task to achieve. To date, several machine learning models have been applied with variable results and no clear indication of which techniques are more appropriate. With the goal of achieving more consistent results, this paper presents the first set of results in an extensive empirical study designed to evaluate the capability of bagging models to increase accuracy prediction over individual models. The study compares two major machine learning based approaches for predicting software maintainability: individual models (regression tree, multilayer perceptron, k-nearest neighbors and m5rules), and an ensemble model (bagging) that are applied to the QUES data set. The results obtained from this study indicate that k-nearest neighbors model outperformed all other individual models. The bagging ensemble model improved accuracy prediction significantly over almost all individual models, and the bagging ensemble models with k-nearest neighbors as a base model achieved superior accurate prediction. This paper also provides a description of the planned programme of research which aims to investigate the performance over various datasets of advanced (ensemble-based) machine learning models

Primary Drivers of Software Maintenance Cost Studied Using Longitudinal Data

We examine the main drivers of software maintenance effort and cost. We use the ‘Distributed Cognition’ framework to hypothesize about how ‘discovery work’ in maintenance is effected by two types of cost drivers: system attributes (size, complexity, age, etc.) and personnel attributes (number of maintainers, location dispersion, etc.). We test our hypotheses using archival data about over 5,000 maintenance projects carried out between 2009 and 2011 on 412 different operational systems in a large financial institution. We find that personnel attributes are significantly more influential than system attributes. In particular, a marginal change in personnel factors is associated with effort growing much faster than cost, indicating an escalating marginal cost of spreading maintenance work across more maintainers and site locations. We also find, counter to expectation, that two system attributes are negatively linked to maintenance effort and cost. Implications of these findings for research and practices are discussed

Effort Estimation Factors for Corrective Software Maintenance Projects: A Qualitative Analysis of Estimation Criteria

In this paper, we identify factors that impact software maintenance effort by exploring expert software maintenance estimators’ knowledge about corrective maintenance projects. We use a qualitative approach to identify the issues important to these experts to derive their effort estimates. We find seventeen factors (rated and rank ordered by importance) that affect corrective maintenance effort and include constructs related to developers, code, defects, and environment. Several of these factors that have a comparably strong influence on corrective maintenance estimation are unique to corrective maintenance and are not generally observed in established software estimation models. The results enhance organizations’ ability to effectively manage maintenance environments by focusing attention on the identified areas. For future research, these results represent an important step toward developing a comprehensive and accurate corrective maintenance effort estimation model

A systematic literature review of machine learning techniques for software maintainability prediction

Context: Software maintainability is one of the fundamental quality attributes of software engineering. The accurate prediction of software maintainability is a significant challenge for the effective management of the software maintenance process. Objective: The major aim of this paper is to present a systematic review of studies related to the prediction of maintainability of object-oriented software systems using machine learning techniques. This review identifies and investigates a number of research questions to comprehensively summarize, analyse and discuss various viewpoints concerning software maintainability measurements, metrics, datasets, evaluation measures, individual models and ensemble models. Method: The review uses the standard systematic literature review method applied to the most common computer science digital database libraries from January 1991 to July 2018. Results: We survey 56 relevant studies in 35 journals and 21 conference proceedings. The results indicate that there is relatively little activity in the area of software maintainability prediction compared with other software quality attributes. CHANGE maintenance effort and the maintainability index were the most commonly used software measurements (dependent variables) employed in the selected primary studies, and most made use of class-level product metrics as the independent variables. Several private datasets were used in the selected studies, and there is a growing demand to publish datasets publicly. Most studies focused on regression problems and performed k-fold cross-validation. Individual prediction models were employed in the majority of studies, while ensemble models relatively rarely. Conclusion: Based on the findings obtained in this systematic literature review, ensemble models demonstrated increased accuracy prediction over individual models, and have been shown to be useful models in predicting software maintainability. However, their application is relatively rare and there is a need to apply these, and other models to an extensive variety of datasets with the aim of improving the accuracy and consistency of results

Identifying effort estimation factors for corrective maintenance in object-oriented systems

This research identifies factors that impact software maintenance effort by exploring the decision-making process of expert estimators of corrective maintenance projects by using qualitative methods to identify the factors that they use in deriving estimates. We implement a technique called causal mapping, which allows us to identify the cognitive links between the information that estimators use, and the estimates that they produce based on that information. Results suggest that a total of 17 factors may be relevant for corrective maintenance effort estimation, covering constructs related to developers, code, defects, and environment. When these factors are rank-ordered, they demonstrate that some of the factors that have greater influence on corrective maintenance estimation, as expressed by expert estimators, are very specific to corrective maintenance and not generally observed in popular software estimation or maintenance estimation models. This line of research aims at addressing the limitations of existing maintenance estimation models that do not incorporate a number of soft factors, thus, achieving less accurate estimates than human experts

Assessing Effort Estimation Models for Corrective Maintenance through Empirical Studies

We present an empirical assessment and improvement of the effort estimation model for corrective maintenance adopted in a major international software enterprise. Our study was composed of two phases. In the first phase we used multiple linear regression analysis to construct effort estimation models validated against real data collected from five corrective maintenance projects. The model previously adopted by the subject company used as predictors the size of the system being maintained and the number of maintenance tasks. While this model was not linear, we show that a linear model including the same variables achieved better performances. Also we show that greater improvements in the model performances can be achieved if the types of the different maintenance tasks is taken into account. In the second phase we performed a replicated assessment of the effort prediction models built in the previous phase on a new corrective maintenance project conducted by the subject company on a software system of the same type as the systems of the previous maintenance projects. The data available for the new project were finer grained, according to the indications devised in the first study. This allowed to improve the confidence in our previous empirical analysis by confirming most of the hypotheses made. The new data also provided other useful indications to better understand the maintenance process of the company in a quantitative way