An effort prediction framework for software defect correction

Abstract

Developers apply changes and updates to software systems to adapt to emerging environments and address new requirements. In turn, these changes introduce additional software defects, usually caused by our inability to comprehend the full scope of the modified code. As a result, software practitioners have developed tools to aid in the detection and prediction of imminent software defects, in addition to the effort required to correct them. Although software development effort prediction has been in use for many years, research into defect-correction effort prediction is relatively new. The increasing complexity, integration and ubiquitous nature of current software systems has sparked renewed interest in this field. Effort prediction now plays a critical role in the planning activities of managers. Accurate predictions help corporations budget, plan and distribute available resources effectively and efficiently. In particular, early defect-correction effort predictions could be used by testers to set schedules, and by managers to plan costs and provide earlier feedback to customers about future releases.In this work, we address the problem of predicting the effort needed to resolve a software defect. More specifically, our study is concerned with defects or issues that are reported on an Issue Tracking System or any other defect repository. Current approaches use one prediction method or technique to produce effort predictions. This approach usually suffers from the weaknesses of the chosen prediction method, and consequently the accuracy of the predictions are affected. To address this problem, we present a composite prediction framework. Rather than using one prediction approach for all defects, we propose the use of multiple integrated methods which complement the weaknesses of one another. Our framework is divided into two sub-categories, Similarity-Score Dependent and Similarity-Score Independent. The Similarity-Score Dependent method utilizes the power of Case-Based Reasoning, also known as Instance-Based Reasoning, to compute predictions. It relies on matching target issues to similar historical cases, then combines their known effort for an informed estimate. On the other hand, the Similarity-Score Independent method makes use of other defect-related information with some statistical manipulation to produce the required estimate. To measure similarity between defects, some method of distance calculation must be used. In some cases, this method might produce misleading results due to observed inconsistencies in history, and the fact that current similarity-scoring techniques cannot account for all the variability in the data. In this case, the Similarity-Score Independent method can be used to estimate the effort, where the effect of such inconsistencies can be reduced.We have performed a number of experimental studies on the proposed framework to assess the effectiveness of the presented techniques. We extracted the data sets from an operational Issue Tracking System in order to test the validity...

Identification and Measurement of Technical Debt Requirements in Software Development: a Systematic Literature Review

“…It becomes possible to notice, for example, that there is a difficulty in managing the TD efficiently, which causes an increase in maintenance costs at a rate that will eventually exceed the delivery value to the client. This difficulty is associated with the TDs measurement because according to some reports, predicting the debt correction effort is often a more challenging task than predicting the effort to develop the software (Hassouna and Tahvildari, 2010). In the following, still, about managing the technical debt of requirements efficiently, primary studies report that unintentional TD is much more problematic to manage than intentional TD.…”

Challenges and opportunities for software change request repositories: a systematic mapping study

“…Finally, the algorithms for reasoning with learning, applied in both types of models, include the following: 0-R [19,95,102], 1-R [19,95,102], naive Bayes [19,89,92,98,101], multilayer perceptron [98], Bayesian networks [95,102], C4.5 [19,89,95,102], J48 [98], PART [89], k-nearest neighbor [97,98,105], linear regression [93,99], SVM [98], and logistic regression [19,95,96,102].…”

“…Then, the time spent to fix the similar CRs can be used to predict the fixing time for the new one. The approaches have used TF‐IDF and LSI for text representation and the cosine similarity function for the reasoning with algebra.…”

Automatic Control of the Software Development Process with Regard to Risk Analysis

“…Nevertheless, the study has low external validity and according to the authors should be considered as a proof of concept. Further research in similar direction was conducted by Hassouna and Tahvildari Hassouna & Tahvildari (2010). The authors conducted an empirical study regarding predicting the effort of defect-fixing in JBoss (the same data set as in Weiss et al (2007)) and Codehaus projects.…”