@PHDTHESIS{Hassaine12-PhD,
   AUTHOR       = {Salima Hassaine},
   SCHOOL       = {Universit� de Montr�al},
   TITLE        = {Design Decay during Software Evolution},
   YEAR         = {2012},
   OPTADDRESS   = {},
   MONTH        = {December},
   NOTE         = {141 pages.},
   OPTTYPE      = {},
   KEYWORDS     = {Evolution patterns, Code and design smells},
   URL          = {http://www.ptidej.net/publications/documents/Thesis+of+Salima+Hassaine.doc.pdf},
   PDF          = {http://www.ptidej.net/publications/documents/Thesis+of+Salima+Hassaine.ppt.pdf},
   ABSTRACT     = {Software systems evolve, requiring continuous 
      maintenance and development. They undergo changes throughout their 
      lifetimes as new features are added and bugs are fixed. As these 
      systems evolved, their designs tend to decay with time and become 
      less adaptable to changing users' requirements. Consequently, 
      software designs become more complex over time and harder to 
      maintain; in some not-so-rare cases, developers prefer redesigning 
      from scratch rather than prolonging the life of existing designs, 
      which causes development and maintenance costs to rise. Therefore, 
      developers must understand the factors that drive the decay of their 
      designs and take proactive steps that facilitate future changes and 
      slow down decay. Design decay occurs when changes are made on a 
      software system by developers who do not understand its original 
      design. On the one hand, making software changes without 
      understanding their effects may lead to the introduction of bugs and 
      the premature retirement of the system. On the other hand, when 
      developers lack knowledge and--or experience in solving a design 
      problem, they may introduce design defects, which are conjectured to 
      have a negative impact on the evolution of systems, which leads to 
      design decay. Thus, developers need mechanisms to understand how a 
      change to a system will impact the rest of the system and tools to 
      detect design defects. In this dissertation, we propose three 
      principal contributions. The \emph{first contribution} aims to 
      evaluate design decay. Measuring design decay consists of using a 
      diagram matching technique to identify structural changes among 
      versions of a design, such as a class diagram. Finding structural 
      changes occurring in long-lived, evolving designs requires the 
      identification of class renamings. Thus, the first step of our 
      approach concerns the identification of class renamings in evolving 
      designs. Then, the second step requires to match several versions of 
      an evolving design to identify decaying and stable parts of the 
      design. We propose bit-vector and incremental clustering algorithms 
      to match several versions of an evolving design. The third step 
      consists of measuring design decay. We propose a set of metrics to 
      evaluate this design decay. The \emph{second contribution} is related 
      to change impact analysis. We present a new metaphor inspired from 
      seismology to identify the change impact. In particular, our approach 
      considers changes to a class as an earthquake that propagates through 
      a long chain of intermediary classes. Our approach combines static 
      dependencies between classes and historical co-change relations to 
      measure the scope of change propagation in a system, \ie{} how far a 
      change propagation will proceed from a ``changed class'' to other 
      classes. The \emph{third contribution} concerns design defects 
      detection. We propose a metaphor inspired from a natural immune 
      system. Like any living creature, designs are subject to diseases, 
      which are design defects. Detection approaches are defense mechanisms 
      of designs. A natural immune system can detect similar pathogens with 
      good precision. This good precision has inspired a family of 
      classification algorithms, Artificial Immune Systems (AIS) 
      algorithms, which we use to detect design defects. The three 
      contributions are evaluated on open-source object-oriented systems 
      and the obtained results enable us to draw the following conclusions: 
      \begin{itemize} \item Design decay metrics, \emph{Tunnel Triplets 
      Metric} ($TTM$) and \emph{Common Triplets Metric} ($CTM$), provide 
      developers useful insights regarding design decay. If $TTM$ 
      decreases, then the original design decays. If $TTM$ is stable, then 
      the original design is stable, which means that the system is more 
      adapted to the new changing requirements. \item Seismology provides 
      an interesting metaphor for change impact analysis. Changes propagate 
      in systems, like earthquakes. The change impact is most severe near 
      the changed class and drops off away from the changed class. Using 
      external information, we show that our approach helps developers to 
      locate easily the change impact. \item Immune system provides an 
      interesting metaphor for detecting design defects. The results of the 
      experiments showed that the precision and recall of our approach are 
      comparable or superior to that of previous approaches. \end{itemize}}
}

{"_id":"Xx5mSBw8nZ5zvCSyE","bibbaseid":"hassaine-designdecayduringsoftwareevolution-2012","downloads":0,"creationDate":"2018-02-22T19:07:02.805Z","title":"Design Decay during Software Evolution","author_short":["Hassaine, S."],"year":2012,"bibtype":"phdthesis","biburl":"http://ptidej.polymtl.ca/yann-gael/Work/Publications/Biblio/complete-bibliography.bib?","bibdata":{"bibtype":"phdthesis","type":"phdthesis","author":[{"firstnames":["Salima"],"propositions":[],"lastnames":["Hassaine"],"suffixes":[]}],"school":"Universit� de Montr�al","title":"Design Decay during Software Evolution","year":"2012","optaddress":"","month":"December","note":"141 pages.","opttype":"","keywords":"Evolution patterns, Code and design smells","url":"http://www.ptidej.net/publications/documents/Thesis+of+Salima+Hassaine.doc.pdf","pdf":"http://www.ptidej.net/publications/documents/Thesis+of+Salima+Hassaine.ppt.pdf","abstract":"Software systems evolve, requiring continuous maintenance and development. They undergo changes throughout their lifetimes as new features are added and bugs are fixed. As these systems evolved, their designs tend to decay with time and become less adaptable to changing users' requirements. Consequently, software designs become more complex over time and harder to maintain; in some not-so-rare cases, developers prefer redesigning from scratch rather than prolonging the life of existing designs, which causes development and maintenance costs to rise. Therefore, developers must understand the factors that drive the decay of their designs and take proactive steps that facilitate future changes and slow down decay. Design decay occurs when changes are made on a software system by developers who do not understand its original design. On the one hand, making software changes without understanding their effects may lead to the introduction of bugs and the premature retirement of the system. On the other hand, when developers lack knowledge and--or experience in solving a design problem, they may introduce design defects, which are conjectured to have a negative impact on the evolution of systems, which leads to design decay. Thus, developers need mechanisms to understand how a change to a system will impact the rest of the system and tools to detect design defects. In this dissertation, we propose three principal contributions. The \\emphfirst contribution aims to evaluate design decay. Measuring design decay consists of using a diagram matching technique to identify structural changes among versions of a design, such as a class diagram. Finding structural changes occurring in long-lived, evolving designs requires the identification of class renamings. Thus, the first step of our approach concerns the identification of class renamings in evolving designs. Then, the second step requires to match several versions of an evolving design to identify decaying and stable parts of the design. We propose bit-vector and incremental clustering algorithms to match several versions of an evolving design. The third step consists of measuring design decay. We propose a set of metrics to evaluate this design decay. The \\emphsecond contribution is related to change impact analysis. We present a new metaphor inspired from seismology to identify the change impact. In particular, our approach considers changes to a class as an earthquake that propagates through a long chain of intermediary classes. Our approach combines static dependencies between classes and historical co-change relations to measure the scope of change propagation in a system, \\ie how far a change propagation will proceed from a ``changed class'' to other classes. The \\emphthird contribution concerns design defects detection. We propose a metaphor inspired from a natural immune system. Like any living creature, designs are subject to diseases, which are design defects. Detection approaches are defense mechanisms of designs. A natural immune system can detect similar pathogens with good precision. This good precision has inspired a family of classification algorithms, Artificial Immune Systems (AIS) algorithms, which we use to detect design defects. The three contributions are evaluated on open-source object-oriented systems and the obtained results enable us to draw the following conclusions: \\beginitemize \\item Design decay metrics, \\emphTunnel Triplets Metric ($TTM$) and \\emphCommon Triplets Metric ($CTM$), provide developers useful insights regarding design decay. If $TTM$ decreases, then the original design decays. If $TTM$ is stable, then the original design is stable, which means that the system is more adapted to the new changing requirements. \\item Seismology provides an interesting metaphor for change impact analysis. Changes propagate in systems, like earthquakes. The change impact is most severe near the changed class and drops off away from the changed class. Using external information, we show that our approach helps developers to locate easily the change impact. \\item Immune system provides an interesting metaphor for detecting design defects. The results of the experiments showed that the precision and recall of our approach are comparable or superior to that of previous approaches. \\enditemize","bibtex":"@PHDTHESIS{Hassaine12-PhD,\r\n AUTHOR = {Salima Hassaine},\r\n SCHOOL = {Universit� de Montr�al},\r\n TITLE = {Design Decay during Software Evolution},\r\n YEAR = {2012},\r\n OPTADDRESS = {},\r\n MONTH = {December},\r\n NOTE = {141 pages.},\r\n OPTTYPE = {},\r\n KEYWORDS = {Evolution patterns, Code and design smells},\r\n URL = {http://www.ptidej.net/publications/documents/Thesis+of+Salima+Hassaine.doc.pdf},\r\n PDF = {http://www.ptidej.net/publications/documents/Thesis+of+Salima+Hassaine.ppt.pdf},\r\n ABSTRACT = {Software systems evolve, requiring continuous \r\n maintenance and development. They undergo changes throughout their \r\n lifetimes as new features are added and bugs are fixed. As these \r\n systems evolved, their designs tend to decay with time and become \r\n less adaptable to changing users' requirements. Consequently, \r\n software designs become more complex over time and harder to \r\n maintain; in some not-so-rare cases, developers prefer redesigning \r\n from scratch rather than prolonging the life of existing designs, \r\n which causes development and maintenance costs to rise. Therefore, \r\n developers must understand the factors that drive the decay of their \r\n designs and take proactive steps that facilitate future changes and \r\n slow down decay. Design decay occurs when changes are made on a \r\n software system by developers who do not understand its original \r\n design. On the one hand, making software changes without \r\n understanding their effects may lead to the introduction of bugs and \r\n the premature retirement of the system. On the other hand, when \r\n developers lack knowledge and--or experience in solving a design \r\n problem, they may introduce design defects, which are conjectured to \r\n have a negative impact on the evolution of systems, which leads to \r\n design decay. Thus, developers need mechanisms to understand how a \r\n change to a system will impact the rest of the system and tools to \r\n detect design defects. In this dissertation, we propose three \r\n principal contributions. The \\emph{first contribution} aims to \r\n evaluate design decay. Measuring design decay consists of using a \r\n diagram matching technique to identify structural changes among \r\n versions of a design, such as a class diagram. Finding structural \r\n changes occurring in long-lived, evolving designs requires the \r\n identification of class renamings. Thus, the first step of our \r\n approach concerns the identification of class renamings in evolving \r\n designs. Then, the second step requires to match several versions of \r\n an evolving design to identify decaying and stable parts of the \r\n design. We propose bit-vector and incremental clustering algorithms \r\n to match several versions of an evolving design. The third step \r\n consists of measuring design decay. We propose a set of metrics to \r\n evaluate this design decay. The \\emph{second contribution} is related \r\n to change impact analysis. We present a new metaphor inspired from \r\n seismology to identify the change impact. In particular, our approach \r\n considers changes to a class as an earthquake that propagates through \r\n a long chain of intermediary classes. Our approach combines static \r\n dependencies between classes and historical co-change relations to \r\n measure the scope of change propagation in a system, \\ie{} how far a \r\n change propagation will proceed from a ``changed class'' to other \r\n classes. The \\emph{third contribution} concerns design defects \r\n detection. We propose a metaphor inspired from a natural immune \r\n system. Like any living creature, designs are subject to diseases, \r\n which are design defects. Detection approaches are defense mechanisms \r\n of designs. A natural immune system can detect similar pathogens with \r\n good precision. This good precision has inspired a family of \r\n classification algorithms, Artificial Immune Systems (AIS) \r\n algorithms, which we use to detect design defects. The three \r\n contributions are evaluated on open-source object-oriented systems \r\n and the obtained results enable us to draw the following conclusions: \r\n \\begin{itemize} \\item Design decay metrics, \\emph{Tunnel Triplets \r\n Metric} ($TTM$) and \\emph{Common Triplets Metric} ($CTM$), provide \r\n developers useful insights regarding design decay. If $TTM$ \r\n decreases, then the original design decays. If $TTM$ is stable, then \r\n the original design is stable, which means that the system is more \r\n adapted to the new changing requirements. \\item Seismology provides \r\n an interesting metaphor for change impact analysis. Changes propagate \r\n in systems, like earthquakes. The change impact is most severe near \r\n the changed class and drops off away from the changed class. Using \r\n external information, we show that our approach helps developers to \r\n locate easily the change impact. \\item Immune system provides an \r\n interesting metaphor for detecting design defects. The results of the \r\n experiments showed that the precision and recall of our approach are \r\n comparable or superior to that of previous approaches. \\end{itemize}}\r\n}\r\n\r\n","author_short":["Hassaine, S."],"key":"Hassaine12-PhD","id":"Hassaine12-PhD","bibbaseid":"hassaine-designdecayduringsoftwareevolution-2012","role":"author","urls":{"Paper":"http://www.ptidej.net/publications/documents/Thesis+of+Salima+Hassaine.doc.pdf"},"keyword":["Evolution patterns","Code and design smells"],"downloads":0},"search_terms":["design","decay","during","software","evolution","hassaine"],"keywords":["evolution patterns","code and design smells"],"authorIDs":[],"dataSources":["ascnA6qYXirdFSqqy"]}