Software testing is an integral part of software development. Not only that testing exists in each software iteration cycle, but it also consumes a considerable amount of resources. While resources such as machinery a...Software testing is an integral part of software development. Not only that testing exists in each software iteration cycle, but it also consumes a considerable amount of resources. While resources such as machinery and manpower are often restricted, it is crucial to decide where and how much effort to put into testing. One way to address this problem is to identify which components of the subject under the test are more error-prone and thus demand more testing efforts. Recent development in machine learning techniques shows promising potential to predict faults in different components of a software system. This work conducts an empirical study to explore the feasibility of using static software metrics to predict software faults. We apply four machine learning techniques to construct fault prediction models from the PROMISE data set and evaluate the effectiveness of using static software metrics to build fault prediction models in four continuous versions of Apache Ant. The empirical results show that the combined software metrics generate the least misclassification errors. The fault prediction results vary significantly among different machine learning techniques and data set. Overall, fault prediction models built with the support vector machine (SVM) have the lowest misclassification errors.展开更多
A desirable software engineering goal is the prediction of software module complexity (a qualitative concept) using automatically generated software metrics (quantitative measurements). This goal may be couched in the...A desirable software engineering goal is the prediction of software module complexity (a qualitative concept) using automatically generated software metrics (quantitative measurements). This goal may be couched in the language of pattern classification;namely, given a set of metrics (a pattern) for a software module, predict the class (level of complexity) to which the module belongs. To find this mapping from metrics to complexity, we present a classification strategy, stochastic metric selection, to determine the subset of software metrics that yields the greatest predictive power with respect to module complexity. We demonstrate the effectiveness of this strategy by empirically evaluating it using a publicly available dataset of metrics compiled from a medical imaging system and comparing the prediction results against several classification system benchmarks.展开更多
Software metrics help us to make meaningful estimates for software products and guide us in taking managerial and technical decisions.However,conventional static metrics have been found to be inadequate for modern obj...Software metrics help us to make meaningful estimates for software products and guide us in taking managerial and technical decisions.However,conventional static metrics have been found to be inadequate for modern object-oriented software due to the presence of object-oriented features such as polymorphism,dynamic binding,inheritance and unused code.This fact motivates us to focus on dynamic metrics in place of traditional static metrics.Moreover,dynamic metrics are more precise than static metrics as they are able to capture the dynamic behaviour of the software system during measurement.These dynamic metrics are usually obtained from the execution traces of the code or from the executable models.In this paper,advantages of dynamic metrics over static metrics are discussed and then a survey of the existing dynamic metrics is carried out.These metrics are characterized into different categories such as dynamic coupling metrics, dynamic cohesion metrics.Towards end of the paper,potential research challenges and opportunities in the field of dynamic metrics are identified.展开更多
To better evaluate the quality of software architecture,a metrics suite is proposed to measure the coupling of software architecture models,in which CBC is used to measure the coupling between components,CBCC is used ...To better evaluate the quality of software architecture,a metrics suite is proposed to measure the coupling of software architecture models,in which CBC is used to measure the coupling between components,CBCC is used to measure the coupling of transferring message between components,CBCCT is used to measure the coupling of software architecture,WCBCC is used to measure the coupling of transferring message with weight between components,and WCBCCT is used to measure the coupling of message transmission with weight in the whole software architecture. The proposed algorithm for the coupling metrics is applied to the design of serve software architecture. Analysis of an example validates the feasibility of this metrics suite.展开更多
Software protection technology has been universally emphasized, with the development of reverse engineering and static analysis techniques. So, it is important to research how to quantitatively evaluate the security o...Software protection technology has been universally emphasized, with the development of reverse engineering and static analysis techniques. So, it is important to research how to quantitatively evaluate the security of the protected software. However, there are some researchers evaluating the security of the proposed protect techniques directly by the traditional complexity metrics, which is not suffident. In order to better reflect security from software complexity, a multi-factor complexity metric based on control flow graph （CFG） is proposed, and the corresponding calculating procedures are presented in detail. Moreover, complexity density models are constructed to indicate the strength of software resisting reverse engineering and code analysis. Instance analysis shows that the proposed method is simple and practical, and can more objectively reflect software security from the perspective of the complexity.展开更多
Architectural design is a crucial issue in software engineering. It makes testing more effective as it contribute to carry out the testing in an early stage of the software development. To improve software testability...Architectural design is a crucial issue in software engineering. It makes testing more effective as it contribute to carry out the testing in an early stage of the software development. To improve software testability, the software architect should consider different testability metrics while building the software architecture. The main objective of this research is to conduct an early assessment of the software architecture for the purpose of its improvement in order to make the testing process more effective. In this paper, an evaluation model to assess software architecture (Architecture Design Testability Evaluation Model (ADTEM)) is presented. ADTEM is based on two different testability metrics: cohesion and coupling. ADTEM consists of two phases: software architecture evaluation phase, and component evaluation phase. In each phase, a fuzzy inference system is used to perform the evaluation process based on cohesion and coupling testing metrics. The model is validated by using a case study: Elders Monitoring System. The experimental results show that ADTEM is efficient and gave a considerable improvement to the software testability process.展开更多
Software is an important part of automotive product development, and it is commonly known that software quality assurance consumes considerable effort in safety-critical embedded software development. Increasing the e...Software is an important part of automotive product development, and it is commonly known that software quality assurance consumes considerable effort in safety-critical embedded software development. Increasing the effectiveness and efficiency of this effort thus becomes more and more important. Identifying problematic code areas which are most likely to fail and therefore require most of the quality assurance attention is required. This article presents an exploratory study investigating whether the faults detected by static analysis tools combined with code complexity metrics can be used as software quality indicators and to build pre-release fault prediction models. The combination of code complexity metrics with static analysis fault density was used to predict the pre-release fault density with an accuracy of 78.3%. This combination was also used to separate high and low quality components with a classification accuracy of 79%.展开更多
A measure of the“goodness”or efficiency of the test suite is used to determine the proficiency of a test suite.The appropriateness of the test suite is determined through mutation analysis.Several Finite State Machi...A measure of the“goodness”or efficiency of the test suite is used to determine the proficiency of a test suite.The appropriateness of the test suite is determined through mutation analysis.Several Finite State Machine(FSM)mutants are produced in mutation analysis by injecting errors against hypotheses.These mutants serve as test subjects for the test suite(TS).The effectiveness of the test suite is proportional to the number of eliminated mutants.The most effective test suite is the one that removes the most significant number of mutants at the optimal time.It is difficult to determine the fault detection ratio of the system.Because it is difficult to identify the system’s potential flaws precisely.In mutation testing,the Fault Detection Ratio(FDR)metric is currently used to express the adequacy of a test suite.However,there are some issues with this metric.If both test suites have the same defect detection rate,the smaller of the two tests is preferred.The test case(TC)is affected by the same issue.The smaller two test cases with identical performance are assumed to have superior performance.Another difficulty involves time.The performance of numerous vehicles claiming to have a perfect mutant capture time is problematic.Our study developed three metrics to address these issues:FDR/|TS|,FDR/|TC|,and FDR/|Time|;In this context,most used test generation tools were examined and tested using the developed metrics.Thanks to the metrics we have developed,the research contributes to eliminating the problems related to performance measurement by integrating the missing parameters into the system.展开更多
Component-based software engineering is concerned with the develop-ment of software that can satisfy the customer prerequisites through reuse or inde-pendent development.Coupling and cohesion measurements are primaril...Component-based software engineering is concerned with the develop-ment of software that can satisfy the customer prerequisites through reuse or inde-pendent development.Coupling and cohesion measurements are primarily used to analyse the better software design quality,increase the reliability and reduced system software complexity.The complexity measurement of cohesion and coupling component to analyze the relationship between the component module.In this paper,proposed the component selection framework of Hexa-oval optimization algorithm for selecting the suitable components from the repository.It measures the interface density modules of coupling and cohesion in a modular software sys-tem.This cohesion measurement has been taken into two parameters for analyz-ing the result of complexity,with the help of low cohesion and high cohesion.In coupling measures between the component of inside parameters and outside parameters.Thefinal process of coupling and cohesion,the measured values were used for the average calculation of components parameter.This paper measures the complexity of direct and indirect interaction among the component as well as the proposed algorithm selecting the optimal component for the repository.The better result is observed for high cohesion and low coupling in compo-nent-based software engineering.展开更多
Evaluating complex information systems necessitates deep contextual knowledge of technology, user needs, and quality. The quality evaluation challenges increase with the system’s complexity, especially when multiple ...Evaluating complex information systems necessitates deep contextual knowledge of technology, user needs, and quality. The quality evaluation challenges increase with the system’s complexity, especially when multiple services supported by varied technological modules, are offered. Existing standards for software quality, such as the ISO25000 series, provide a broad framework for evaluation. Broadness offers initial implementation ease albeit, it often lacks specificity to cater to individual system modules. This paper maps 48 data metrics and 175 software metrics on specific system modules while aligning them with ISO standard quality traits. Using the ISO25000 series as a foundation, especially ISO25010 and 25012, this research seeks to augment the applicability of these standards to multi-faceted systems, exemplified by five distinct software modules prevalent in modern information ecosystems.展开更多
By analyzing the average percent of faults detected （APFD） metric and its variant versions, which are widely utilized as metrics to evaluate the fault detection efficiency of the test suite, this paper points out so...By analyzing the average percent of faults detected （APFD） metric and its variant versions, which are widely utilized as metrics to evaluate the fault detection efficiency of the test suite, this paper points out some limitations of the APFD series metrics. These limitations include APFD series metrics having inaccurate physical explanations and being unable to precisely describe the process of fault detection. To avoid the limitations of existing metrics, this paper proposes two improved metrics for evaluating fault detection efficiency of a test suite, including relative-APFD and relative-APFDc. The proposed metrics refer to both the speed of fault detection and the constraint of the testing source. The case study shows that the two proposed metrics can provide much more precise descriptions of the fault detection process and the fault detection efficiency of the test suite.展开更多
An essential objective of software development is to locate and fix defects ahead of schedule that could be expected under diverse circumstances. Many software development activities are performed by individuals, whic...An essential objective of software development is to locate and fix defects ahead of schedule that could be expected under diverse circumstances. Many software development activities are performed by individuals, which may lead to different software bugs over the development to occur, causing disappointments in the not-so-distant future. Thus, the prediction of software defects in the first stages has become a primary interest in the field of software engineering. Various software defect prediction (SDP) approaches that rely on software metrics have been proposed in the last two decades. Bagging, support vector machines (SVM), decision tree (DS), and random forest (RF) classifiers are known to perform well to predict defects. This paper studies and compares these supervised machine learning and ensemble classifiers on 10 NASA datasets. The experimental results showed that, in the majority of cases, RF was the best performing classifier compared to the others.展开更多
Detecting well-known design patterns in object-oriented program source code can help maintainers understand the design of a program. Through the detection, the understandability, maintainability, and reusability of ob...Detecting well-known design patterns in object-oriented program source code can help maintainers understand the design of a program. Through the detection, the understandability, maintainability, and reusability of object-oriented programs can be improved. There are automated detection techniques;however, many existing techniques are based on static analysis and use strict conditions composed on class structure data. Hence, it is difficult for them to detect and distinguish design patterns in which the class structures are similar. Moreover, it is difficult for them to deal with diversity in design pattern applications. To solve these problems in existing techniques, we propose a design pattern detection technique using source code metrics and machine learning. Our technique judges candidates for the roles that compose design patterns by using machine learning and measurements of several metrics, and it detects design patterns by analyzing the relations between candidates. It suppresses false negatives and distinguishes patterns in which the class structures are similar. As a result of experimental evaluations with a set of programs, we confirmed that our technique is more accurate than two conventional techniques.展开更多
In order to evaluate the structural complexity of class diagrams systematically and deeply, a new guiding framework of structural complexity is presented. An index system of structural complexity for class diagrams is...In order to evaluate the structural complexity of class diagrams systematically and deeply, a new guiding framework of structural complexity is presented. An index system of structural complexity for class diagrams is given. This article discusses the formal description of class diagrams, and presents the method of formally structural complexity metrics for class diagrams from associations, dependencies, aggregations, generalizations and so on. An applicable example proves the feasibility of the presented method.展开更多
This paper addresses the challenge of estimating eventual software system size during a development project. The ap-proach is to build a family of estimation models that use information about architectural design char...This paper addresses the challenge of estimating eventual software system size during a development project. The ap-proach is to build a family of estimation models that use information about architectural design characteristics of the evolving software product as leading indicators of system size. Four models were developed to provide an increasingly accurate size estimate throughout the design process. Multivariate regression analyses were conducted using 21 Ada subsystems, totaling 183,000 lines of code. The models explain from 47% of the variation in delivered software size early in the design phase, to 89% late in the design phase.展开更多
In software engineering, software measures are often proposed without precise identification of the measurable concepts they attempt to quantify: consequently, the numbers obtained are challenging to reproduce in diff...In software engineering, software measures are often proposed without precise identification of the measurable concepts they attempt to quantify: consequently, the numbers obtained are challenging to reproduce in different measurement contexts and to interpret, either as base measures or in combination as derived measures. The lack of consistency when using base measures in data collection can affect both data preparation and data analysis. This paper analyzes the similarities and differences across three different views of measurement methods (ISO International Vocabulary on Metrology, ISO 15939, and ISO 25021), and uses a process proposed for the design of software measurement methods to analyze two examples of such methods selected from the literature.展开更多
In the software engineering literature, it is commonly believed that economies of scale do not occur in case of software Development and Enhancement Projects （D＆EP）. Their per-unit cost does not decrease but increa...In the software engineering literature, it is commonly believed that economies of scale do not occur in case of software Development and Enhancement Projects （D＆EP）. Their per-unit cost does not decrease but increase with the growth of such projects product size. Thus this is diseconomies of scale that occur in them. The significance of this phenomenon results from the fact that it is commonly considered to be one of the fundamental objective causes of their low effectiveness. This is of particular significance with regard to Business Software Systems （BSS） D＆EP characterized by exceptionally low effectiveness comparing to other software D＆EP. Thus the paper aims at answering the following two questions： （1） Do economies of scale really not occur in BSS D＆EP？ （2） If economies of scale may occur in BSS D＆EP, what factors are then promoting them？ These issues classify into economics problems of software engineering research and practice.展开更多
Reliability engineering implemented early in the development process has a significant impact on improving software quality.It can assist in the design of architecture and guide later testing,which is beyond the scope...Reliability engineering implemented early in the development process has a significant impact on improving software quality.It can assist in the design of architecture and guide later testing,which is beyond the scope of traditional reliability analysis methods.Structural reliability models work for this,but most of them remain tested in only simulation case studies due to lack of actual data.Here we use software metrics for reliability modeling which are collected from source codes of post versions.Through the proposed strategy,redundant metric elements are filtered out and the rest are aggregated to represent the module reliability.We further propose a framework to automatically apply the module value and calculate overall reliability by introducing formal methods.The experimental results from an actual project show that reliability analysis at the design and development stage can be close to the validity of analysis at the test stage through reasonable application of metric data.The study also demonstrates that the proposed methods have good applicability.展开更多
The quality element of a design class is important as it has a substantial listed influence on overall required quality of the software. The complexity and lengthy codes of program severely affect several quality meas...The quality element of a design class is important as it has a substantial listed influence on overall required quality of the software. The complexity and lengthy codes of program severely affect several quality measures, especially efficiency of the final delivered software. This paper does an appropriate review on efficiency of object oriented software, and suggests some relevant information about class design level efficiency. Program efficiency is an important factor to software quality. Moreover, if software efficiency analysis has been done in the early steps of the software creation process then it assures as a criterion to software quality. This article proposes a comprehensive research idea for estimation of the efficiency index. Subsequently, it does an extremely methodical review on design efficiency of software. After that, a design metric based efficiency estimation model at the design time has been proposed by creating the link between design properties of the program and efficiency criteria. Furthermore, the correlation is mapped among all with the help of statistical values for selected design properties and efficiency criteria. In addition, efficiency estimation model is empirically validated with try-out data.展开更多
文摘Software testing is an integral part of software development. Not only that testing exists in each software iteration cycle, but it also consumes a considerable amount of resources. While resources such as machinery and manpower are often restricted, it is crucial to decide where and how much effort to put into testing. One way to address this problem is to identify which components of the subject under the test are more error-prone and thus demand more testing efforts. Recent development in machine learning techniques shows promising potential to predict faults in different components of a software system. This work conducts an empirical study to explore the feasibility of using static software metrics to predict software faults. We apply four machine learning techniques to construct fault prediction models from the PROMISE data set and evaluate the effectiveness of using static software metrics to build fault prediction models in four continuous versions of Apache Ant. The empirical results show that the combined software metrics generate the least misclassification errors. The fault prediction results vary significantly among different machine learning techniques and data set. Overall, fault prediction models built with the support vector machine (SVM) have the lowest misclassification errors.
文摘A desirable software engineering goal is the prediction of software module complexity (a qualitative concept) using automatically generated software metrics (quantitative measurements). This goal may be couched in the language of pattern classification;namely, given a set of metrics (a pattern) for a software module, predict the class (level of complexity) to which the module belongs. To find this mapping from metrics to complexity, we present a classification strategy, stochastic metric selection, to determine the subset of software metrics that yields the greatest predictive power with respect to module complexity. We demonstrate the effectiveness of this strategy by empirically evaluating it using a publicly available dataset of metrics compiled from a medical imaging system and comparing the prediction results against several classification system benchmarks.
文摘Software metrics help us to make meaningful estimates for software products and guide us in taking managerial and technical decisions.However,conventional static metrics have been found to be inadequate for modern object-oriented software due to the presence of object-oriented features such as polymorphism,dynamic binding,inheritance and unused code.This fact motivates us to focus on dynamic metrics in place of traditional static metrics.Moreover,dynamic metrics are more precise than static metrics as they are able to capture the dynamic behaviour of the software system during measurement.These dynamic metrics are usually obtained from the execution traces of the code or from the executable models.In this paper,advantages of dynamic metrics over static metrics are discussed and then a survey of the existing dynamic metrics is carried out.These metrics are characterized into different categories such as dynamic coupling metrics, dynamic cohesion metrics.Towards end of the paper,potential research challenges and opportunities in the field of dynamic metrics are identified.
基金Sponsored by the Science and Technology Department Term of Education of Heilongjiang Province(Grant No. 10541098)
文摘To better evaluate the quality of software architecture,a metrics suite is proposed to measure the coupling of software architecture models,in which CBC is used to measure the coupling between components,CBCC is used to measure the coupling of transferring message between components,CBCCT is used to measure the coupling of software architecture,WCBCC is used to measure the coupling of transferring message with weight between components,and WCBCCT is used to measure the coupling of message transmission with weight in the whole software architecture. The proposed algorithm for the coupling metrics is applied to the design of serve software architecture. Analysis of an example validates the feasibility of this metrics suite.
基金Key Project of the National Eleventh-Five Year Research Program of China(No.2006BAD10A07)
文摘Software protection technology has been universally emphasized, with the development of reverse engineering and static analysis techniques. So, it is important to research how to quantitatively evaluate the security of the protected software. However, there are some researchers evaluating the security of the proposed protect techniques directly by the traditional complexity metrics, which is not suffident. In order to better reflect security from software complexity, a multi-factor complexity metric based on control flow graph （CFG） is proposed, and the corresponding calculating procedures are presented in detail. Moreover, complexity density models are constructed to indicate the strength of software resisting reverse engineering and code analysis. Instance analysis shows that the proposed method is simple and practical, and can more objectively reflect software security from the perspective of the complexity.
文摘Architectural design is a crucial issue in software engineering. It makes testing more effective as it contribute to carry out the testing in an early stage of the software development. To improve software testability, the software architect should consider different testability metrics while building the software architecture. The main objective of this research is to conduct an early assessment of the software architecture for the purpose of its improvement in order to make the testing process more effective. In this paper, an evaluation model to assess software architecture (Architecture Design Testability Evaluation Model (ADTEM)) is presented. ADTEM is based on two different testability metrics: cohesion and coupling. ADTEM consists of two phases: software architecture evaluation phase, and component evaluation phase. In each phase, a fuzzy inference system is used to perform the evaluation process based on cohesion and coupling testing metrics. The model is validated by using a case study: Elders Monitoring System. The experimental results show that ADTEM is efficient and gave a considerable improvement to the software testability process.
文摘Software is an important part of automotive product development, and it is commonly known that software quality assurance consumes considerable effort in safety-critical embedded software development. Increasing the effectiveness and efficiency of this effort thus becomes more and more important. Identifying problematic code areas which are most likely to fail and therefore require most of the quality assurance attention is required. This article presents an exploratory study investigating whether the faults detected by static analysis tools combined with code complexity metrics can be used as software quality indicators and to build pre-release fault prediction models. The combination of code complexity metrics with static analysis fault density was used to predict the pre-release fault density with an accuracy of 78.3%. This combination was also used to separate high and low quality components with a classification accuracy of 79%.
文摘A measure of the“goodness”or efficiency of the test suite is used to determine the proficiency of a test suite.The appropriateness of the test suite is determined through mutation analysis.Several Finite State Machine(FSM)mutants are produced in mutation analysis by injecting errors against hypotheses.These mutants serve as test subjects for the test suite(TS).The effectiveness of the test suite is proportional to the number of eliminated mutants.The most effective test suite is the one that removes the most significant number of mutants at the optimal time.It is difficult to determine the fault detection ratio of the system.Because it is difficult to identify the system’s potential flaws precisely.In mutation testing,the Fault Detection Ratio(FDR)metric is currently used to express the adequacy of a test suite.However,there are some issues with this metric.If both test suites have the same defect detection rate,the smaller of the two tests is preferred.The test case(TC)is affected by the same issue.The smaller two test cases with identical performance are assumed to have superior performance.Another difficulty involves time.The performance of numerous vehicles claiming to have a perfect mutant capture time is problematic.Our study developed three metrics to address these issues:FDR/|TS|,FDR/|TC|,and FDR/|Time|;In this context,most used test generation tools were examined and tested using the developed metrics.Thanks to the metrics we have developed,the research contributes to eliminating the problems related to performance measurement by integrating the missing parameters into the system.
基金We deeply acknowledge Taif University for Supporting this research through Taif University Researchers Supporting Project number(TURSP-2020/231),Taif University,Taif,Saudi Arabia.
文摘Component-based software engineering is concerned with the develop-ment of software that can satisfy the customer prerequisites through reuse or inde-pendent development.Coupling and cohesion measurements are primarily used to analyse the better software design quality,increase the reliability and reduced system software complexity.The complexity measurement of cohesion and coupling component to analyze the relationship between the component module.In this paper,proposed the component selection framework of Hexa-oval optimization algorithm for selecting the suitable components from the repository.It measures the interface density modules of coupling and cohesion in a modular software sys-tem.This cohesion measurement has been taken into two parameters for analyz-ing the result of complexity,with the help of low cohesion and high cohesion.In coupling measures between the component of inside parameters and outside parameters.Thefinal process of coupling and cohesion,the measured values were used for the average calculation of components parameter.This paper measures the complexity of direct and indirect interaction among the component as well as the proposed algorithm selecting the optimal component for the repository.The better result is observed for high cohesion and low coupling in compo-nent-based software engineering.
文摘Evaluating complex information systems necessitates deep contextual knowledge of technology, user needs, and quality. The quality evaluation challenges increase with the system’s complexity, especially when multiple services supported by varied technological modules, are offered. Existing standards for software quality, such as the ISO25000 series, provide a broad framework for evaluation. Broadness offers initial implementation ease albeit, it often lacks specificity to cater to individual system modules. This paper maps 48 data metrics and 175 software metrics on specific system modules while aligning them with ISO standard quality traits. Using the ISO25000 series as a foundation, especially ISO25010 and 25012, this research seeks to augment the applicability of these standards to multi-faceted systems, exemplified by five distinct software modules prevalent in modern information ecosystems.
基金The National Natural Science Foundation of China(No.61300054)the Natural Science Foundation of Jiangsu Province(No.BK2011190,BK20130879)+1 种基金the Natural Science Foundation of Higher Education Institutions of Jiangsu Province(No.13KJB520018)the Science Foundation of Nanjing University of Posts&Telecommunications(No.NY212023)
文摘By analyzing the average percent of faults detected （APFD） metric and its variant versions, which are widely utilized as metrics to evaluate the fault detection efficiency of the test suite, this paper points out some limitations of the APFD series metrics. These limitations include APFD series metrics having inaccurate physical explanations and being unable to precisely describe the process of fault detection. To avoid the limitations of existing metrics, this paper proposes two improved metrics for evaluating fault detection efficiency of a test suite, including relative-APFD and relative-APFDc. The proposed metrics refer to both the speed of fault detection and the constraint of the testing source. The case study shows that the two proposed metrics can provide much more precise descriptions of the fault detection process and the fault detection efficiency of the test suite.
文摘An essential objective of software development is to locate and fix defects ahead of schedule that could be expected under diverse circumstances. Many software development activities are performed by individuals, which may lead to different software bugs over the development to occur, causing disappointments in the not-so-distant future. Thus, the prediction of software defects in the first stages has become a primary interest in the field of software engineering. Various software defect prediction (SDP) approaches that rely on software metrics have been proposed in the last two decades. Bagging, support vector machines (SVM), decision tree (DS), and random forest (RF) classifiers are known to perform well to predict defects. This paper studies and compares these supervised machine learning and ensemble classifiers on 10 NASA datasets. The experimental results showed that, in the majority of cases, RF was the best performing classifier compared to the others.
文摘Detecting well-known design patterns in object-oriented program source code can help maintainers understand the design of a program. Through the detection, the understandability, maintainability, and reusability of object-oriented programs can be improved. There are automated detection techniques;however, many existing techniques are based on static analysis and use strict conditions composed on class structure data. Hence, it is difficult for them to detect and distinguish design patterns in which the class structures are similar. Moreover, it is difficult for them to deal with diversity in design pattern applications. To solve these problems in existing techniques, we propose a design pattern detection technique using source code metrics and machine learning. Our technique judges candidates for the roles that compose design patterns by using machine learning and measurements of several metrics, and it detects design patterns by analyzing the relations between candidates. It suppresses false negatives and distinguishes patterns in which the class structures are similar. As a result of experimental evaluations with a set of programs, we confirmed that our technique is more accurate than two conventional techniques.
基金Science and Technology Department Term of Education of Heilongjiang Province(Grant No.11511127)
文摘In order to evaluate the structural complexity of class diagrams systematically and deeply, a new guiding framework of structural complexity is presented. An index system of structural complexity for class diagrams is given. This article discusses the formal description of class diagrams, and presents the method of formally structural complexity metrics for class diagrams from associations, dependencies, aggregations, generalizations and so on. An applicable example proves the feasibility of the presented method.
文摘This paper addresses the challenge of estimating eventual software system size during a development project. The ap-proach is to build a family of estimation models that use information about architectural design characteristics of the evolving software product as leading indicators of system size. Four models were developed to provide an increasingly accurate size estimate throughout the design process. Multivariate regression analyses were conducted using 21 Ada subsystems, totaling 183,000 lines of code. The models explain from 47% of the variation in delivered software size early in the design phase, to 89% late in the design phase.
文摘In software engineering, software measures are often proposed without precise identification of the measurable concepts they attempt to quantify: consequently, the numbers obtained are challenging to reproduce in different measurement contexts and to interpret, either as base measures or in combination as derived measures. The lack of consistency when using base measures in data collection can affect both data preparation and data analysis. This paper analyzes the similarities and differences across three different views of measurement methods (ISO International Vocabulary on Metrology, ISO 15939, and ISO 25021), and uses a process proposed for the design of software measurement methods to analyze two examples of such methods selected from the literature.
文摘In the software engineering literature, it is commonly believed that economies of scale do not occur in case of software Development and Enhancement Projects （D＆EP）. Their per-unit cost does not decrease but increase with the growth of such projects product size. Thus this is diseconomies of scale that occur in them. The significance of this phenomenon results from the fact that it is commonly considered to be one of the fundamental objective causes of their low effectiveness. This is of particular significance with regard to Business Software Systems （BSS） D＆EP characterized by exceptionally low effectiveness comparing to other software D＆EP. Thus the paper aims at answering the following two questions： （1） Do economies of scale really not occur in BSS D＆EP？ （2） If economies of scale may occur in BSS D＆EP, what factors are then promoting them？ These issues classify into economics problems of software engineering research and practice.
基金This work was supported by the National Natural Science Foundation of China(61572167)the National Key Research and Development Program of China(2016YFC0801804)the Natural Science Foundation for Anhui Higher Education Institutions of China(KJ2019A0482).
文摘Reliability engineering implemented early in the development process has a significant impact on improving software quality.It can assist in the design of architecture and guide later testing,which is beyond the scope of traditional reliability analysis methods.Structural reliability models work for this,but most of them remain tested in only simulation case studies due to lack of actual data.Here we use software metrics for reliability modeling which are collected from source codes of post versions.Through the proposed strategy,redundant metric elements are filtered out and the rest are aggregated to represent the module reliability.We further propose a framework to automatically apply the module value and calculate overall reliability by introducing formal methods.The experimental results from an actual project show that reliability analysis at the design and development stage can be close to the validity of analysis at the test stage through reasonable application of metric data.The study also demonstrates that the proposed methods have good applicability.
文摘The quality element of a design class is important as it has a substantial listed influence on overall required quality of the software. The complexity and lengthy codes of program severely affect several quality measures, especially efficiency of the final delivered software. This paper does an appropriate review on efficiency of object oriented software, and suggests some relevant information about class design level efficiency. Program efficiency is an important factor to software quality. Moreover, if software efficiency analysis has been done in the early steps of the software creation process then it assures as a criterion to software quality. This article proposes a comprehensive research idea for estimation of the efficiency index. Subsequently, it does an extremely methodical review on design efficiency of software. After that, a design metric based efficiency estimation model at the design time has been proposed by creating the link between design properties of the program and efficiency criteria. Furthermore, the correlation is mapped among all with the help of statistical values for selected design properties and efficiency criteria. In addition, efficiency estimation model is empirically validated with try-out data.
