Clustering Dycom: An Online Cross-Company Software Effort Estimation Study

Background: Software Effort Estimation (SEE) can be formulated as an online learning problem, where new projects are completed over time and may become available for training. In this scenario, a Cross-Company (CC) SEE approach called Dycom can drastically reduce the number of Within-Company (WC) projects needed for training, saving the high cost of collecting such training projects. However, Dycom relies on splitting CC projects into different subsets in order to create its CC models. Such splitting can have a significant impact on Dycom's predictive performance. Aims: This paper investigates whether clustering methods can be used to help finding good CC splits for Dycom. Method: Dycom is extended to use clustering methods for creating the CC subsets. Three different clustering methods are investigated, namely Hierarchical Clustering, K-Means, and Expectation-Maximisation. Clustering Dycom is compared against the original Dycom with CC subsets of different sizes, based on four SEE databases. A baseline WC model is also included in the analysis. Results: Clustering Dycom with K-Means can potentially help to split the CC projects, managing to achieve similar or better predictive performance than Dycom. However, K-Means still requires the number of CC subsets to be pre-defined, and a poor choice can negatively affect predictive performance. EM enables Dycom to automatically set the number of CC subsets while still maintaining or improving predictive performance with respect to the baseline WC model. Clustering Dycom with Hierarchical Clustering did not offer significant advantage in terms of predictive performance. Conclusion: Clustering methods can be an effective way to automatically generate Dycom's CC subsets

How to make best use of cross-company data in software effort estimation?

Previous works using Cross-Company (CC) data for making Within-Company (WC) Software Effort Estimation (SEE) try to use CC data or models directly to provide predictions in the WC context. So, these data or models are only helpful when they match the WC context well. When they do not, a fair amount of WC training data, which are usually expensive to acquire, are still necessary to achieve good performance. We investigate how to make best use of CC data, so that we can reduce the amount of WC data while maintaining or improving performance in comparison to WC SEE models. This is done by proposing a new framework to learn the relationship between CC and WC projects explicitly, allowing CC models to be mapped to the WC context. Such mapped models can be useful even when the CC models themselves do not match the WC context directly. Our study shows that a new approach instantiating this framework is able not only to use substantially less WC data than a corresponding WC model, but also to achieve similar/better performance. This approach can also be used to provide insight into the behaviour of a company in comparison to others

Which Models of the Past Are Relevant to the Present? A software effort estimation approach to exploiting useful past models

Software Effort Estimation (SEE) models can be used for decision-support by software managers to determine the effort required to develop a software project. They are created based on data describing projects completed in the past. Such data could include past projects from within the company that we are interested in (WC projects) and/or from other companies (cross-company, i.e., CC projects). In particular, the use of CC data has been investigated in an attempt to overcome limitations caused by the typically small size of WC datasets. However, software companies operate in non-stationary environments, where changes may affect the typical effort required to develop software projects. Our previous work showed that both WC and CC models of the past can become more or less useful over time, i.e., they can sometimes be helpful and sometimes misleading. So, how can we know if and when a model created based on past data represents well the current projects being estimated? We propose an approach called Dynamic Cross-company Learning (DCL) to dynamically identify which WC or CC past models are most useful for making predictions to a given company at the present. DCL automatically emphasizes the predictions given by these models in order to improve predictive performance. Our experiments comparing DCL against existing WC and CC approaches show that DCL is successful in improving SEE by emphasizing the most useful past models. A thorough analysis of DCL’s behaviour is provided, strengthening its external validity

Which Models of the Past Are Relevant to the Present? A software effort estimation approach to exploiting useful past models

Software Effort Estimation (SEE) models can be used for decision-support by software managers to determine the effort required to develop a software project. They are created based on data describing projects completed in the past. Such data could include past projects from within the company that we are interested in (WC projects) and/or from other companies (cross-company, i.e., CC projects). In particular, the use of CC data has been investigated in an attempt to overcome limitations caused by the typically small size of WC datasets. However, software companies operate in non-stationary environments, where changes may affect the typical effort required to develop software projects. Our previous work showed that both WC and CC models of the past can become more or less useful over time, i.e., they can sometimes be helpful and sometimes misleading. So, how can we know if and when a model created based on past data represents well the current projects being estimated? We propose an approach called Dynamic Cross-company Learning (DCL) to dynamically identify which WC or CC past models are most useful for making predictions to a given company at the present. DCL automatically emphasizes the predictions given by these models in order to improve predictive performance. Our experiments comparing DCL against existing WC and CC approaches show that DCL is successful in improving SEE by emphasizing the most useful past models. A thorough analysis of DCL’s behaviour is provided, strengthening its external validity

Resampling-based ensemble methods for online class imbalance learning

Online class imbalance learning is a new learning problem that combines the challenges of both online learning and class imbalance learning. It deals with data streams having very skewed class distributions. This type of problems commonly exists in real-world applications, such as fault diagnosis of real-time control monitoring systems and intrusion detection in computer networks. In our earlier work, we defined class imbalance online, and proposed two learning algorithms OOB and UOB that build an ensemble model overcoming class imbalance in real time through resampling and time-decayed metrics. In this paper, we further improve the resampling strategy inside OOB and UOB, and look into their performance in both static and dynamic data streams. We give the first comprehensive analysis of class imbalance in data streams, in terms of data distributions, imbalance rates and changes in class imbalance status. We find that UOB is better at recognizing minority-class examples in static data streams, and OOB is more robust against dynamic changes in class imbalance status. The data distribution is a major factor affecting their performance. Based on the insight gained, we then propose two new ensemble methods that maintain both OOB and UOB with adaptive weights for final predictions, called WEOB1 and WEOB2. They are shown to possess the strength of OOB and UOB with good accuracy and robustness

A Systematic Study of Online Class Imbalance Learning with Concept Drift

As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift. It deals with data streams having very skewed class distributions, where concept drift may occur. It has recently received increased research attention; however, very little work addresses the combined problem where both class imbalance and concept drift coexist. As the first systematic study of handling concept drift in class-imbalanced data streams, this paper first provides a comprehensive review of current research progress in this field, including current research focuses and open challenges. Then, an in-depth experimental study is performed, with the goal of understanding how to best overcome concept drift in online learning with class imbalance. Based on the analysis, a general guideline is proposed for the development of an effective algorithm

A Novel Automated Approach for Software Effort Estimation Based on Data Augmentation

Software effort estimation (SEE) usually suffers from data scarcity problem due to the expensive or long process of data collection. As a result, companies usually have limited projects for effort estimation, causing unsatisfactory prediction performance. Few studies have investigated strategies to generate additional SEE data to aid such learning. We aim to propose a synthetic data generator to address the data scarcity problem of SEE. Our synthetic generator enlarges the SEE data set size by slightly displacing some randomly chosen training examples. It can be used with any SEE method as a data preprocessor. Its effectiveness is justified with 6 state-of-the-art SEE models across 14 SEE data sets. We also compare our data generator against the only existing approach in the SEE literature. Experimental results show that our synthetic projects can significantly improve the performance of some SEE methods especially when the training data is insufficient. When they cannot significantly improve the prediction performance, they are not detrimental either. Besides, our synthetic data generator is significantly superior or perform similarly to its competitor in the SEE literature. Therefore, our data generator plays a non-harmful if not significantly beneficial effect on the SEE methods investigated in this paper. Therefore, it is helpful in addressing the data scarcity problem of SEE

Guest editorial: special issue on predictive models for software quality

[First paragraph] Software systems are increasingly large and complex, making activities related to ensuring software quality increasingly difficult. In this context, techniques able to automatically retrieve knowledge from software data in order to improve software quality are highly desirable. Predictive modelling has been showing promising results in this area. For instance, it can be used to learn the relationship between features retrieved from software processes, software usage or software itself and certain properties of interest, e.g., the presence of bugs, the likelihood of changes leading to crashes and the presence of code smells. Such knowledge can be particularly useful to improve the quality of large and complex systems

A Systematic Study of Online Class Imbalance Learning with Concept Drift

As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift. It deals with data streams having very skewed class distributions, where concept drift may occur. It has recently received increased research attention; however, very little work addresses the combined problem where both class imbalance and concept drift coexist. As the first systematic study of handling concept drift in class-imbalanced data streams, this paper first provides a comprehensive review of current research progress in this field, including current research focuses and open challenges. Then, an in-depth experimental study is performed, with the goal of understanding how to best overcome concept drift in online learning with class imbalance

Data Mining for Software Engineering and Humans in the Loop

The field of data mining for software engineering has been growing over the last decade. This field is concerned with the use of data mining to provide useful insights into how to improve software engineering processes and software itself, supporting decision making. For that, data produced by software engineering processes and products during and after software development is used. Despite promising results, there is frequently a lack of discussion on the role of software engineering practitioners amidst the data mining approaches. This makes adoption of data mining by software engineering practitioners difficult. Moreover, the fact that experts’ knowledge is frequently ignored by data mining approaches, together with the lack of transparency of such approaches, can hinder the acceptability of data mining by software engineering practitioners. In order to overcome these problems, this position paper provides a discussion of the role of software engineering experts when adopting data mining approaches. It also argues that this role can be extended in order to increase experts’ involvement in the process of building data mining models. We believe that such extended involvement is not only likely to increase software engineers’ acceptability of the resulting models, but also improve the models themselves. We also provide some recommendations aimed at increasing the success of experts involvement and model acceptability