Information theoretic metrics for software architectures

Shereshevsky, M.; Ammari, Habib M.; Gradetsky, N.; Mili, Ali; Ammar, H.H.

doi:10.1109/cmpsac.2001.960611

Cited by 15 publications

(7 citation statements)

References 7 publications

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“…et al [13] Graph-based Maintainability, Bug severity MD=2, MQ=1, LV=3, USB=2, CA=1, AP=3, TS=2 Elish [22] Internal structure Understandability MD=-, MQ=1, LV=4, USB=3, CA=1, AP=2, TS=2 Gupta et al [27] Internal structure Understandability MD=3, MQ=1, LV=2, USB=2, CA=1, AP=2, TS=2 Gupta et al [28] Internal structure Reusability MD=3, MQ=3, LV=1, USB=2, CA=2, AP=2, TS=2 Haohua et al [29] Graph-based Complexity MD=2, MQ=1, LV=3, USB=2, CA=1, AP=2, TS=2 Hu et al [30] Specific model Maintainability MD=2, MQ=2, LV=2, USB=2, CA=1, AP=2, TS=1 Hwa et al [31] Internal structure Understandability MD=2, MQ=3, LV=2, USB=2, CA=1, AP=2, TS=2 Kanjilal et al [32] Graph-based Complexity MD=2, MQ=1, LV=2, USB=3, CA=1, AP=2, TS=1 Lindvall et al [44] Internal structure Maintainability MD=1, MQ=3, LV=2, USB=2, CA=1, AP=3, TS=2 Ma et al [45,46] Graph-based Complexity, Fault rate MD=-, MQ=1, LV=4, USB=2, CA=2, AP=2, TS=2 Misic [47] Internal structure External coherence MD=3, MQ=1, LV=1, USB=3, CA=1, AP=2, TS=1 Reddy et al [52] Graph-based Complexity MD=2, MQ=3, LV=1, USB=2, CA=2, AP=2, TS=1 Salman [54] Internal structure Maintainability, Integrability MD=3, MQ=1, LV=3, USB=3, CA=1, AP=2, TS=1 Sarkar et al [57] Internal structure Modularization MD=2, MQ=1, LV=3, USB=2, CA=1, AP=2, TS=2 Sartipi [58] Graph-based Modularization MD=2, MQ=1, LV=2, USB=2, CA=1, AP=3, TS=2 Sheresh. et al [60] Specific model -MD=2, MQ=2, LV=-, USB=1, CA=1, AP=1, TS=1 Wei et al [61] Graph-based -MD=2, MQ=1, LV=-, USB=2, CA=1, AP=1, TS=1 Yu et al [62] Specific model Fault rate MD=1, MQ=1, LV=2, USB=2, CA=1, AP=2, TS=2 Zhou et al [63] Graph-based External coherence MD=3, MQ=1, LV=2, USB=2, CA=2, AP=2, TS=2…”

Section: Referencementioning

confidence: 99%

“…Sartipi [58] considers component as a group of system entities in form of a file (to evaluate a design), or module and subsystem (to evaluate the architecture). Shereshevsky et al [60] consider primitive components (at the lowest level) that exchange the information between each other and do not contain any other components and upper-level components that contain those at the level below them without overlapping. Wei et al [61] consider components as autonomous pieces of software code with well-defined functionality and interfaces, similarly to the work by Kanjilal et al At the end Yu et al [62] consider primitive components, that represent the smallest units, and can be composed into compound components that are further composed into higher level compound components so that the layered component structure is formed (similarly to the work by Shereshevsky et al).…”

Section: Measured Software Artefacts and Attributes (Rq1)mentioning

confidence: 99%

“…they require some effort in order to understand and compute the metrics, and 16 % of the studies contain metrics that can be easily used in projects (counting based metrics). For example, Shereshevsky et al [60] defined metrics that are very difficult to use. They defined coupling and cohesion metrics based on Shannon entropy, for information exchange between the components in the architecture.…”

Section: Measured Software Artefacts and Attributes (Rq1)mentioning

confidence: 99%

See 2 more Smart Citations

Software metrics for measuring the understandability of architectural structures

Stevanetic

Zdun

2015

Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering

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The main idea of software architecture is to concentrate on the "big picture" of a software system. In the context of object-oriented software systems higher-level architectural structures or views above the level of classes are frequently used to capture the "big picture" of the system. One of the critical aspects of these higher-level views is understandability, as one of their main purposes is to enable designers to abstract away fine-grained details. In this article we present a systematic mapping study on software metrics related to the understandability concepts of such higher-level software structures with regard to their relations to the system implementation. In our systematic mapping study, we started from 3951 studies obtained using an electronic search in the four digital libraries from ACM, IEEE, Scopus, and Springer. After applying our inclusion/exclusion criteria as well as the snowballing technique we selected 268 studies for in-depth study. From those, we selected 25 studies that contain relevant metrics. We classify the identified studies and metrics with regard to the measured artefacts, attributes, quality characteristics, and representation model used for the metrics definitions. Additionally, we present the assessment of the maturity level of the identified studies. Overall, there is a lack of maturity in the studies. We discuss possible techniques how to mitigate the identified problems. From the academic point of view we believe that our study is a good starting point for future studies aiming at improving the existing works. From a practitioner's point of view, the results of our study can be used as a catalogue and an indication of the maturity of the existing research results.

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Section: Referencementioning

confidence: 99%

Section: Measured Software Artefacts and Attributes (Rq1)mentioning

confidence: 99%

Section: Measured Software Artefacts and Attributes (Rq1)mentioning

confidence: 99%

See 1 more Smart Citation

Software metrics for measuring the understandability of architectural structures

Stevanetic

Zdun

2015

Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering

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“…Shereshevshky et al propose dynamic measures of "coupling" and "cohesion" based on entropy (Shereshevshky et al, 2001). Their abstraction of software is a graph based on information flows among components where the probability of each flow is based on operational use of the software, and thus, depends on program dynamics.…”

Section: Related Workmentioning

confidence: 99%

Measuring size, complexity, and coupling of hypergraph abstractions of software: An information-theory approach

Allen

Gottipati²,

Govindarajan³

2007

Software Qual J

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Software development is fundamentally based on cognitive processes. Our motivating hypothesis is that amounts of various kinds of information in software artifacts may have useful statistical relationships with software-engineering attributes. This paper proposes measures of size, complexity and coupling in terms of the amount of information, building on formal definitions of these software-metric families proposed by Briand, Morasca, and Basili.Ordinary graphs represent relationships between pairs of nodes. We extend prior work with ordinary graphs to hypergraphs representing relationships among sets of nodes. Some software engineering abstractions, such as set-use relations for public variables, are better represented as hypergraphs than ordinary (binary) graphs.Traditional software metrics are based on counting. In contrast, we adopt information theory as the basis for measurement, because the design decisions embodied by software are information. This paper proposes software metrics of size, complexity, and coupling based on information in the pattern of incident hyperedges. For comparison, we also define corresponding counting-based metrics.Three exploratory case studies illustrate some of the distinctive features of the proposed metrics. The case studies found that information theory-based software metrics make distinctions that counting metrics do not, which may be relevant to software engineering quality and process. We also identify situations when information theory-based metrics are simply proportional to corresponding counting metrics.

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“…We also analyze and compare change propagation metric with respect to other coupling-based metrics. [12], product line properties [18], etc.…”

mentioning

confidence: 99%

Web-based tool for software architecture metrics

Shaik¹

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Information theoretic metrics for software architectures

Cited by 15 publications

References 7 publications

Software metrics for measuring the understandability of architectural structures

Software metrics for measuring the understandability of architectural structures

Measuring size, complexity, and coupling of hypergraph abstractions of software: An information-theory approach

Web-based tool for software architecture metrics

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