5,356 research outputs found
Estimation of Defect proneness Using Design complexity Measurements in Object- Oriented Software
Software engineering is continuously facing the challenges of growing
complexity of software packages and increased level of data on defects and
drawbacks from software production process. This makes a clarion call for
inventions and methods which can enable a more reusable, reliable, easily
maintainable and high quality software systems with deeper control on software
generation process. Quality and productivity are indeed the two most important
parameters for controlling any industrial process. Implementation of a
successful control system requires some means of measurement. Software metrics
play an important role in the management aspects of the software development
process such as better planning, assessment of improvements, resource
allocation and reduction of unpredictability. The process involving early
detection of potential problems, productivity evaluation and evaluating
external quality factors such as reusability, maintainability, defect proneness
and complexity are of utmost importance. Here we discuss the application of CK
metrics and estimation model to predict the external quality parameters for
optimizing the design process and production process for desired levels of
quality. Estimation of defect-proneness in object-oriented system at design
level is developed using a novel methodology where models of relationship
between CK metrics and defect-proneness index is achieved. A multifunctional
estimation approach captures the correlation between CK metrics and defect
proneness level of software modules.Comment: 5 pages, 1 figur
A Review of Metrics and Modeling Techniques in Software Fault Prediction Model Development
This paper surveys different software fault predictions progressed through different data analytic techniques reported in the software engineering literature. This study split in three broad areas; (a) The description of software metrics suites reported and validated in the literature. (b) A brief outline of previous research published in the development of software fault prediction model based on various analytic techniques. This utilizes the taxonomy of analytic techniques while summarizing published research. (c) A review of the advantages of using the combination of metrics. Though, this area is comparatively new and needs more research efforts
Identification, Analysis & Empirical Validation (IAV) of Object Oriented Design (OO) Metrics as Quality Indicators
Metrics and Measure are closely inter-related to each other. Measure is defined as way of defining amount, dimension, capacity or size of some attribute of a product in quantitative manner while Metric is unit used for measuring attribute. Software quality is one of the major concerns that need to be addressed and measured. Object oriented (OO) systems require effective metrics to assess quality of software. The paper is designed to identify attributes and measures that can help in determining and affecting quality attributes. The paper conducts empirical study by taking public dataset KC1 from NASA project database. It is validated by applying statistical techniques like correlation analysis and regression analysis. After analysis of data, it is found that metrics SLOC, RFC, WMC and CBO are significant and treated as quality indicators while metrics DIT and NOC are not significant. The results produced from them throws significant impact on improving software quality
Evolutionary Computing based an Efficient and Cost Effective Software Defect Prediction System
The earlier defect prediction and fault removal can play a vital role in ensuring software reliability and quality of service In this paper Hybrid Evolutionary computing based Neural Network HENN based software defect prediction model has been developed For HENN an adaptive genetic algorithm A-GA has been developed that alleviates the key existing limitations like local minima and convergence Furthermore the implementation of A-GA enables adaptive crossover and mutation probability selection that strengthens computational efficiency of our proposed system The proposed HENN algorithm has been used for adaptive weight estimation and learning optimization in ANN for defect prediction In addition a novel defect prediction and fault removal cost estimation model has been derived to evaluate the cost effectiveness of the proposed system The simulation results obtained for PROMISE and NASA MDP datasets exhibit the proposed model outperforms Levenberg Marquardt based ANN system LM-ANN and other systems as well And also cost analysis exhibits that the proposed HENN model is approximate 21 66 cost effective as compared to LM-AN
Are Smell-Based Metrics Actually Useful in Effort-Aware Structural Change-Proneness Prediction? An Empirical Study
Bad code smells (also named as code smells) are symptoms of poor design choices in implementation. Existing studies empirically confirmed that the presence of code smells increases the likelihood of subsequent changes (i.e., change-proness). However, to the best of our knowledge, no prior studies have leveraged smell-based metrics to predict particular change type (i.e., structural changes). Moreover, when evaluating the effectiveness of smell-based metrics in structural change-proneness prediction, none of existing studies take into account of the effort inspecting those change-prone source code. In this paper, we consider five smell-based metrics for effort-aware structural change-proneness prediction and compare these metrics with a baseline of well-known CK metrics in predicting particular categories of change types. Specifically, we first employ univariate logistic regression to analyze the correlation between each smellbased metric and structural change-proneness. Then, we build multivariate prediction models to examine the effectiveness of smell-based metrics in effort-aware structural change-proneness prediction when used alone and used together with the baseline metrics, respectively. Our experiments are conducted on six Java open-source projects with up to 60 versions and results indicate that: (1) all smell-based metrics are significantly related to structural change-proneness, except metric ANS in hive and SCM in camel after removing confounding effect of file size; (2) in most cases, smell-based metrics outperform the baseline metrics in predicting structural change-proneness; and (3) when used together with the baseline metrics, the smell-based metrics are more effective to predict change-prone files with being aware of inspection effort
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