An algorithmic approach to missing data problem in modeling human aspects in software development

Abstract

Background: In our previous research, we built defect prediction models by using confirmation bias metrics. Due to confirmation bias developers tend to perform unit tests to make their programs run rather than breaking their code. This, in turn, leads to an increase in defect density. The performance of prediction model that is built using confirmation bias was as good as the models that were built with static code or churn metrics. Aims: Collection of confirmation bias metrics may result in partially "missing data" due to developers' tight schedules, evaluation apprehension and lack of motivation as well as staff turnover. In this paper, we employ ExpectationMaximization (EM) algorithm to impute missing confirmation bias data. Method: We used four datasets from two large-scale companies. For each dataset, we generated all possible missing data configurations and then employed Roweis' EM algorithm to impute missing data. We built defect prediction models using the imputed data. We compared the performances of our proposed models with the ones that used complete data. Results: In all datasets, when missing data percentage is less than or equal to 50% on average, our proposed model that used imputed data yielded performance results that are comparable with the performance results of the models that used complete data. Conclusions: We may encounter the "missing data" problem in building defect prediction models. Our results in this study showed that instead of discarding missing or noisy data, in our case confirmation bias metrics, we can use effective techniques such as EM based imputation to overcome this problem.

Development of Rule-Based Software Risk Assessment and Management Method with Fuzzy Inference System

“…Reliable code writing has been investigated about the impact of software project outputs and work style on recognizing software risks. Moreover, Calikli and Bener [53] have shown some important points in software risk assessment and management. ese points are software project development plan and software team psychology.…”

Software Risk Assessment and Management with Rules Based on Fuzzy Approach

“…Calikli and Bener have shown some important points in software risk assessment and management. These points; software project development plan and software team psychology [23]. (4 significant software risks have been determined.…”

Data Mining based Inferences about Software Parameters

“…He has searched the effect of reliable code writing, software project outcomes and working style to measurement and evaluation of software development [19]. Calikli and Bener (2013) have showed some important points in measurement and evaluation of software development process in their study. Those points are about software project development schedule and software development team psychology [20].…”

“…Calikli and Bener (2013) have showed some important points in measurement and evaluation of software development process in their study. Those points are about software project development schedule and software development team psychology [20].…”