Semantic metrics for software products

Mili, Ali; Jaoua, Ali; Frias, Marcelo F.; Helali, Rasha Gaffer Mohammed

doi:10.1007/s11334-014-0233-3

Cited by 12 publications

(12 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e proposed reliability prediction models use software metrics (called independent variables) to evaluate measurable reliability attributes (called dependent variable) like defect density, fault-proneness, and defect count [4, 8-10, 14, 30]. Mili et al [16] also proposed a suite of four metrics to monitor programs reliability based on their redundancy.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…Furthermore, in 2015, redundancy was also used to identify the semantic similarity of code fragments [36]. e redundancy metrics proposed by Mili et al [16,22] assess program information redundancy provided by the different states of the program [34]. Program states are the set of variables manipulating related data and reflect the uncertainty about the outcome of these variables.…”

Section: Software Redundancymentioning

confidence: 99%

“…Program states are the set of variables manipulating related data and reflect the uncertainty about the outcome of these variables. To develop a better understanding of the redundancy metrics, we present the terminology related to program states: (i) State space is defined by a set of values that the declared program variables may take [16]. (ii) Initial state space is the state of the program (function/class) represented by the input variables [22].…”

Section: Software Redundancymentioning

confidence: 99%

“…Continuous research studies focusing on the assessment and prediction of software reliability are needed. erefore, a suite of four semantic metrics (language-independent) are proposed in [16] to assess programs redundancy in order to monitor their reliability. However, these metrics are theoretically presented and manually computed for basic arithmetic operations.…”

Section: Introductionmentioning

confidence: 99%

“…Literature review shows a lack of software metrics which are proposed for reliability measurement and prediction. In this context, a suite of four semantic language-independent metrics was proposed by Mili et al (2014) to assess program redundancy using Shannon entropy measure. e main objective of these metrics is to monitor program reliability.…”

mentioning

confidence: 99%

See 4 more Smart Citations

An Empirical Assessment and Validation of Redundancy Metrics Using Defect Density as Reliability Indicator

Amara

Fatnassi

Rabai

2021

Scientific Programming

View full text Add to dashboard Cite

Software metrics which are language-dependent are proposed as quantitative measures to assess internal quality factors for both method and class levels like cohesion and complexity. The external quality factors like reliability and maintainability are in general predicted using different metrics of internal attributes. Literature review shows a lack of software metrics which are proposed for reliability measurement and prediction. In this context, a suite of four semantic language-independent metrics was proposed by Mili et al. (2014) to assess program redundancy using Shannon entropy measure. The main objective of these metrics is to monitor program reliability. Despite their important purpose, they are manually computed and only theoretically validated. Therefore, this paper aims to assess the redundancy metrics and empirically validate them as significant reliability indicators. As software reliability is an external attribute that cannot be directly evaluated, we employ other measurable quality factors that represent direct reflections of this attribute. Among these factors, defect density is widely used to measure and predict software reliability based on software metrics. Therefore, a linear regression technique is used to show the usefulness of these metrics as significant indicators of software defect density. A quantitative model is then proposed to predict software defect density based on redundancy metrics in order to monitor software reliability.

show abstract

Section: Related Work and Motivationmentioning

confidence: 99%