An Effort Prediction Framework for Software Defect Correction

Effort Prediction;Software Development;Electrical and Computer Engineering

Hassouna, Alaa

University of Waterloo

关键词: Effort Prediction;    Software Development;    Electrical and Computer Engineering;   

Others  :  https://uwspace.uwaterloo.ca/bitstream/10012/3935/1/An_Effort_Prediction_Framework_for_Software_Defect_Correction.pdf

瑞士|英语

来源: UWSPACE Waterloo Institutional Repository

【 摘 要 】

Developers apply changes and updates to software systems to adapt to emergingenvironments and address new requirements. In turn, these changes introduceadditional software defects, usually caused by our inability to comprehend the fullscope of the modi ed code. As a result, software practitioners have developed toolsto aid in the detection and prediction of imminent software defects, in addition tothe eort required to correct them. Although software development eort predictionhas been in use for many years, research into defect-correction eort prediction isrelatively new. The increasing complexity, integration and ubiquitous nature ofcurrent software systems has sparked renewed interest in thiseld. Eort predictionnow plays a critical role in the planning activities of managers. Accurate predictionshelp corporations budget, plan and distribute available resources eectively ande ciently. In particular, early defect-correction eort predictions could be used bytesters to set schedules, and by managers to plan costs and provide earlier feedbackto customers about future releases.In this work, we address the problem of predicting the eort needed to resolve asoftware defect. More speci cally, our study is concerned with defects or issues thatare reported on an Issue Tracking System or any other defect repository. Currentapproaches use one prediction method or technique to produce eort predictions.This approach usually suers from the weaknesses of the chosen prediction method,and consequently the accuracy of the predictions are aected. To address this problem,we present a composite prediction framework. Rather than using one predictionapproach for all defects, we propose the use of multiple integrated methodswhich complement the weaknesses of one another. Our framework is divided intotwo 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 onmatching target issues to similar historical cases, then combines their known eortfor an informed estimate. On the other hand, the Similarity-Score Independentmethod makes use of other defect-related information with some statistical manipulationto produce the required estimate. To measure similarity between defects,some method of distance calculation must be used. In some cases, this methodmight produce misleading results due to observed inconsistencies in history, andthe fact that current similarity-scoring techniques cannot account for all the variabilityin the data. In this case, the Similarity-Score Independent method can beused to estimate the eort, where the eect of such inconsistencies can be reduced.We have performed a number of experimental studies on the proposed frameworkto assess the eectiveness of the presented techniques. We extracted the data setsfrom an operational Issue Tracking System in order to test the validity of the modelon real project data. These studies involved the development of multiple tools inboth the Java programming language and PHP, each for a certain stage of dataanalysis and manipulation. The results show that our proposed approach producessigni cant improvements when compared to current methods.