Workshop MTD 2012 – Author Index |
Contents -
Abstracts -
Authors
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Bäck, Thomas |
![]() Jelle de Groot, Ariadi Nugroho, Thomas Bäck, and Joost Visser (Software Improvement Group, Netherlands; Leiden University, Netherlands; Radboud University Nijmegen, Netherlands) Assessment of the economic value of software systems is useful in contexts such as capitalization on the balance sheet and due diligence prior to acquisition. Current accounting practice in determining software value is based on the cost spent in software development. This approach fails to account for the efficiency with which software has been produced or the quality of the product. This paper proposes three alternative models for determining the production value of software, based on the notions of technical debt and interest. We applied the models to 367 proprietary systems developed by a range of different organisations using a range of different programming languages. We present the valuation results and discuss the weaknesses and strengths of the models. ![]() |
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Bhansali, Sanjay |
![]() J. David Morgenthaler, Misha Gridnev, Raluca Sauciuc, and Sanjay Bhansali (Google, USA) With a large and rapidly changing codebase, Google software engineers are constantly paying interest on various forms of technical debt. Google engineers also make efforts to pay down that debt, whether through special Fixit days, or via dedicated teams, variously known as janitors, cultivators, or demolition experts. We describe several related efforts to measure and pay down technical debt found in Google's BUILD files and associated dead code. We address debt found in dependency specifications, unbuildable targets, and unnecessary command line flags. These efforts often expose other forms of technical debt that must first be managed. ![]() |
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Brondum, John |
![]() John Brondum and Liming Zhu (NICTA, Australia; University of New South Wales, Australia) Visibility of technical debt is critical. A lack thereof can lead to significant problems without adequate visibility as part of the system level decision-making processes [2]. Current approaches for analysing and monitoring architecture related debt are based on dependency analysis to detect code level violations of the software architecture [2,3,6]. However, heterogeneous environments with several systems constructed using COTS, and/or several programming languages may not offer sufficient code visibility. Other limiting factors include legal contracts, Intellectual Property Rights, and just very large systems. Secondly, the complexity of a software dependency is often greater than simple structural dependencies, including; multi-dimensional properties (as argued by [10]); behavioural dependencies [5,9]; and ‘implicit’ dependencies (i.e., dependency inter-relatedness [11]). This paper proposes a simple modelling approach for visualising dependency relationships as an extension of the current approaches, while supporting complex dependencies. The model can be built using existing dependency analysis and general architectural knowledge; thus is better suited for heterogeneous environments. We demonstrate the proposed modelling using an exemplar, and two field case studies. ![]() |
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Cai, Yuanfang |
![]() Clemente Izurieta, Antonio Vetrò, Nico Zazworka, Yuanfang Cai, Carolyn B. Seaman, and Forrest Shull (Montana State University, USA; Politecnico di Torino, Italy; Fraunhofer CESE, USA; Drexel University, USA; University of Maryland in Baltimore County, USA) To date, several methods and tools for detecting source code and design anomalies have been developed. While each method focuses on identifying certain classes of source code anomalies that potentially relate to technical debt (TD), the overlaps and gaps among these classes and TD have not been rigorously demonstrated. We propose to construct a seminal technical debt landscape as a way to visualize and organize research on the subject. ![]() ![]() Carolyn B. Seaman, Yuepu Guo, Clemente Izurieta, Yuanfang Cai, Nico Zazworka, Forrest Shull, and Antonio Vetrò (University of Maryland in Baltimore County, USA; Montana State University, USA; Drexel University, USA; Fraunhofer CESE, USA; Politecnico di Torino, Italy) The management of technical debt ultimately requires decision making – about incurring, paying off, or deferring technical debt instances. This position paper discusses several existing approaches to complex decision making, and suggests that exploring their applicability to technical debt decision making would be a worthwhile subject for further research. ![]() |
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Curtis, Bill |
![]() Bill Curtis, Jay Sappidi, and Alexandra Szynkarski (CAST, USA; CAST, France) This study summarizes results of a study of Technical Debt across 745 business applications comprising 365 million lines of code collected from 160 companies in 10 industry segments. These applications were submitted to a static analysis that evaluates quality within and across application layers that may be coded in different languages. The analysis consists of evaluating the application against a repository of over 1200 rules of good architectural and coding practice. A formula for estimating Technical Debt with adjustable parameters is presented. Results are presented for Technical Debt across the entire sample as well as for different programming languages and quality factors. ![]() |
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De Groot, Jelle |
![]() Jelle de Groot, Ariadi Nugroho, Thomas Bäck, and Joost Visser (Software Improvement Group, Netherlands; Leiden University, Netherlands; Radboud University Nijmegen, Netherlands) Assessment of the economic value of software systems is useful in contexts such as capitalization on the balance sheet and due diligence prior to acquisition. Current accounting practice in determining software value is based on the cost spent in software development. This approach fails to account for the efficiency with which software has been produced or the quality of the product. This paper proposes three alternative models for determining the production value of software, based on the notions of technical debt and interest. We applied the models to 367 proprietary systems developed by a range of different organisations using a range of different programming languages. We present the valuation results and discuss the weaknesses and strengths of the models. ![]() |
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Ernst, Neil A. |
![]() Neil A. Ernst (University of British Columbia, Canada) Technical debt is the trading of long-term software quality in favor of short-term expediency. While the concept has traditionally been applied to tradeoffs at the code and architecture phases, it also manifests itself in the system requirements analysis phase. Little attention has been paid to requirements over time in software: requirements are often badly out of synch with the implementation, or not used at all. However, requirements are the ultimate validation of project success, since they are the manifestation of the stakeholder’s desires for the system. In this position paper, we define technical debt in requirements as the distance between the implementation and the actual state of the world. We highlight how a requirements modeling tool, RE-KOMBINE, makes requirements, domain constraints and implementation first-class concerns. RE-KOMBINE represents technical debt using the notion of optimal solutions to a requirements problem. We show how this interpretation of technical debt may be useful in deciding how much requirements analysis is sufficient. ![]() |
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Ferme, Vincenzo |
![]() Francesca Arcelli Fontana, Vincenzo Ferme, and Stefano Spinelli (University of Milano-Bicocca, Italy; Blue Reply, Italy) Different forms of technical debt exist that have to be carefully managed. In this paper we focus our attention on design debt, represented by code smells. We consider three smells that we detect in open source systems of different domains. Our principal aim is to give advice on which design debt has to be paid first, according to the three smells we have analyzed. Moreover, we discuss if the detection of these smells could be tailored to the specific application domain of a system. ![]() |
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Fontana, Francesca Arcelli |
![]() Francesca Arcelli Fontana, Vincenzo Ferme, and Stefano Spinelli (University of Milano-Bicocca, Italy; Blue Reply, Italy) Different forms of technical debt exist that have to be carefully managed. In this paper we focus our attention on design debt, represented by code smells. We consider three smells that we detect in open source systems of different domains. Our principal aim is to give advice on which design debt has to be paid first, according to the three smells we have analyzed. Moreover, we discuss if the detection of these smells could be tailored to the specific application domain of a system. ![]() |
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Gridnev, Misha |
![]() J. David Morgenthaler, Misha Gridnev, Raluca Sauciuc, and Sanjay Bhansali (Google, USA) With a large and rapidly changing codebase, Google software engineers are constantly paying interest on various forms of technical debt. Google engineers also make efforts to pay down that debt, whether through special Fixit days, or via dedicated teams, variously known as janitors, cultivators, or demolition experts. We describe several related efforts to measure and pay down technical debt found in Google's BUILD files and associated dead code. We address debt found in dependency specifications, unbuildable targets, and unnecessary command line flags. These efforts often expose other forms of technical debt that must first be managed. ![]() |
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Guo, Yuepu |
![]() Carolyn B. Seaman, Yuepu Guo, Clemente Izurieta, Yuanfang Cai, Nico Zazworka, Forrest Shull, and Antonio Vetrò (University of Maryland in Baltimore County, USA; Montana State University, USA; Drexel University, USA; Fraunhofer CESE, USA; Politecnico di Torino, Italy) The management of technical debt ultimately requires decision making – about incurring, paying off, or deferring technical debt instances. This position paper discusses several existing approaches to complex decision making, and suggests that exploring their applicability to technical debt decision making would be a worthwhile subject for further research. ![]() ![]() Will Snipes, Brian Robinson, Yuepu Guo, and Carolyn B. Seaman (ABB, USA; University of Maryland in Baltimore County, USA) Making a decision about whether to fix or defer fixing a defect is important to software projects. Deferring defects accumulates a technical debt that burdens the software team and customer with a less than optimal solution. The decision to defer fixing a defect is made by Software Change Control Boards (CCBs) based on a set of decision factors. In this paper, we evaluated the set of decision factors used by two CCBs at ABB in the context of technical debt management. The aim was to determine how a model of cost and benefits of incurring technical debt could be part of the CCB decision process. We identified the cost categories and decision factors for fixing and deferring defects as a result of interviews with CCB members. We found that the decision factors could incorporate the financial aspects when using the technical debt metaphor. We identify opportunities for further research to integrate technical debt concepts with the decision factors towards better long term outcomes. ![]() |
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Izurieta, Clemente |
![]() Clemente Izurieta, Antonio Vetrò, Nico Zazworka, Yuanfang Cai, Carolyn B. Seaman, and Forrest Shull (Montana State University, USA; Politecnico di Torino, Italy; Fraunhofer CESE, USA; Drexel University, USA; University of Maryland in Baltimore County, USA) To date, several methods and tools for detecting source code and design anomalies have been developed. While each method focuses on identifying certain classes of source code anomalies that potentially relate to technical debt (TD), the overlaps and gaps among these classes and TD have not been rigorously demonstrated. We propose to construct a seminal technical debt landscape as a way to visualize and organize research on the subject. ![]() ![]() Carolyn B. Seaman, Yuepu Guo, Clemente Izurieta, Yuanfang Cai, Nico Zazworka, Forrest Shull, and Antonio Vetrò (University of Maryland in Baltimore County, USA; Montana State University, USA; Drexel University, USA; Fraunhofer CESE, USA; Politecnico di Torino, Italy) The management of technical debt ultimately requires decision making – about incurring, paying off, or deferring technical debt instances. This position paper discusses several existing approaches to complex decision making, and suggests that exploring their applicability to technical debt decision making would be a worthwhile subject for further research. ![]() |
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Letouzey, Jean-Louis |
![]() Jean-Louis Letouzey (inspearit, France) This paper presents the SQALE (Software Quality Assessment Based on Lifecycle Expectations) method. We describe its Quality Model and Analysis Model which are used to estimate the Quality and the Technical Debt of an application source code. We provide recommendations and guidelines for using the SQALE indicators in order to analyse the structure and the impact of the Technical Debt. ![]() |
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McGregor, John D. |
![]() John D. McGregor, J. Yates Monteith, and Jie Zhang (Clemson University, USA) The members of the ecosystem encompassing our organization are affected by our decisions just as we are affected by their decisions. If an organization takes on technical debt with respect to a specific asset, that decision will affect users of the asset either directly or indirectly. In this position paper we distinguish between incurring technical debt directly and experiencing the effects of technical debt indirectly. We illustrate why two separate concepts are needed for a complete theory and provide examples from ecosystem models we have created for several organizations. The result is a model that produces good explanations for posited scenarios. ![]() |
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Monteith, J. Yates |
![]() John D. McGregor, J. Yates Monteith, and Jie Zhang (Clemson University, USA) The members of the ecosystem encompassing our organization are affected by our decisions just as we are affected by their decisions. If an organization takes on technical debt with respect to a specific asset, that decision will affect users of the asset either directly or indirectly. In this position paper we distinguish between incurring technical debt directly and experiencing the effects of technical debt indirectly. We illustrate why two separate concepts are needed for a complete theory and provide examples from ecosystem models we have created for several organizations. The result is a model that produces good explanations for posited scenarios. ![]() |
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Morgenthaler, J. David |
![]() J. David Morgenthaler, Misha Gridnev, Raluca Sauciuc, and Sanjay Bhansali (Google, USA) With a large and rapidly changing codebase, Google software engineers are constantly paying interest on various forms of technical debt. Google engineers also make efforts to pay down that debt, whether through special Fixit days, or via dedicated teams, variously known as janitors, cultivators, or demolition experts. We describe several related efforts to measure and pay down technical debt found in Google's BUILD files and associated dead code. We address debt found in dependency specifications, unbuildable targets, and unnecessary command line flags. These efforts often expose other forms of technical debt that must first be managed. ![]() |
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Nugroho, Ariadi |
![]() Jelle de Groot, Ariadi Nugroho, Thomas Bäck, and Joost Visser (Software Improvement Group, Netherlands; Leiden University, Netherlands; Radboud University Nijmegen, Netherlands) Assessment of the economic value of software systems is useful in contexts such as capitalization on the balance sheet and due diligence prior to acquisition. Current accounting practice in determining software value is based on the cost spent in software development. This approach fails to account for the efficiency with which software has been produced or the quality of the product. This paper proposes three alternative models for determining the production value of software, based on the notions of technical debt and interest. We applied the models to 367 proprietary systems developed by a range of different organisations using a range of different programming languages. We present the valuation results and discuss the weaknesses and strengths of the models. ![]() |
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Robinson, Brian |
![]() Will Snipes, Brian Robinson, Yuepu Guo, and Carolyn B. Seaman (ABB, USA; University of Maryland in Baltimore County, USA) Making a decision about whether to fix or defer fixing a defect is important to software projects. Deferring defects accumulates a technical debt that burdens the software team and customer with a less than optimal solution. The decision to defer fixing a defect is made by Software Change Control Boards (CCBs) based on a set of decision factors. In this paper, we evaluated the set of decision factors used by two CCBs at ABB in the context of technical debt management. The aim was to determine how a model of cost and benefits of incurring technical debt could be part of the CCB decision process. We identified the cost categories and decision factors for fixing and deferring defects as a result of interviews with CCB members. We found that the decision factors could incorporate the financial aspects when using the technical debt metaphor. We identify opportunities for further research to integrate technical debt concepts with the decision factors towards better long term outcomes. ![]() |
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Sappidi, Jay |
![]() Bill Curtis, Jay Sappidi, and Alexandra Szynkarski (CAST, USA; CAST, France) This study summarizes results of a study of Technical Debt across 745 business applications comprising 365 million lines of code collected from 160 companies in 10 industry segments. These applications were submitted to a static analysis that evaluates quality within and across application layers that may be coded in different languages. The analysis consists of evaluating the application against a repository of over 1200 rules of good architectural and coding practice. A formula for estimating Technical Debt with adjustable parameters is presented. Results are presented for Technical Debt across the entire sample as well as for different programming languages and quality factors. ![]() |
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Sauciuc, Raluca |
![]() J. David Morgenthaler, Misha Gridnev, Raluca Sauciuc, and Sanjay Bhansali (Google, USA) With a large and rapidly changing codebase, Google software engineers are constantly paying interest on various forms of technical debt. Google engineers also make efforts to pay down that debt, whether through special Fixit days, or via dedicated teams, variously known as janitors, cultivators, or demolition experts. We describe several related efforts to measure and pay down technical debt found in Google's BUILD files and associated dead code. We address debt found in dependency specifications, unbuildable targets, and unnecessary command line flags. These efforts often expose other forms of technical debt that must first be managed. ![]() |
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Seaman, Carolyn B. |
![]() Clemente Izurieta, Antonio Vetrò, Nico Zazworka, Yuanfang Cai, Carolyn B. Seaman, and Forrest Shull (Montana State University, USA; Politecnico di Torino, Italy; Fraunhofer CESE, USA; Drexel University, USA; University of Maryland in Baltimore County, USA) To date, several methods and tools for detecting source code and design anomalies have been developed. While each method focuses on identifying certain classes of source code anomalies that potentially relate to technical debt (TD), the overlaps and gaps among these classes and TD have not been rigorously demonstrated. We propose to construct a seminal technical debt landscape as a way to visualize and organize research on the subject. ![]() ![]() Carolyn B. Seaman, Yuepu Guo, Clemente Izurieta, Yuanfang Cai, Nico Zazworka, Forrest Shull, and Antonio Vetrò (University of Maryland in Baltimore County, USA; Montana State University, USA; Drexel University, USA; Fraunhofer CESE, USA; Politecnico di Torino, Italy) The management of technical debt ultimately requires decision making – about incurring, paying off, or deferring technical debt instances. This position paper discusses several existing approaches to complex decision making, and suggests that exploring their applicability to technical debt decision making would be a worthwhile subject for further research. ![]() ![]() Will Snipes, Brian Robinson, Yuepu Guo, and Carolyn B. Seaman (ABB, USA; University of Maryland in Baltimore County, USA) Making a decision about whether to fix or defer fixing a defect is important to software projects. Deferring defects accumulates a technical debt that burdens the software team and customer with a less than optimal solution. The decision to defer fixing a defect is made by Software Change Control Boards (CCBs) based on a set of decision factors. In this paper, we evaluated the set of decision factors used by two CCBs at ABB in the context of technical debt management. The aim was to determine how a model of cost and benefits of incurring technical debt could be part of the CCB decision process. We identified the cost categories and decision factors for fixing and deferring defects as a result of interviews with CCB members. We found that the decision factors could incorporate the financial aspects when using the technical debt metaphor. We identify opportunities for further research to integrate technical debt concepts with the decision factors towards better long term outcomes. ![]() |
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Shull, Forrest |
![]() Clemente Izurieta, Antonio Vetrò, Nico Zazworka, Yuanfang Cai, Carolyn B. Seaman, and Forrest Shull (Montana State University, USA; Politecnico di Torino, Italy; Fraunhofer CESE, USA; Drexel University, USA; University of Maryland in Baltimore County, USA) To date, several methods and tools for detecting source code and design anomalies have been developed. While each method focuses on identifying certain classes of source code anomalies that potentially relate to technical debt (TD), the overlaps and gaps among these classes and TD have not been rigorously demonstrated. We propose to construct a seminal technical debt landscape as a way to visualize and organize research on the subject. ![]() ![]() Carolyn B. Seaman, Yuepu Guo, Clemente Izurieta, Yuanfang Cai, Nico Zazworka, Forrest Shull, and Antonio Vetrò (University of Maryland in Baltimore County, USA; Montana State University, USA; Drexel University, USA; Fraunhofer CESE, USA; Politecnico di Torino, Italy) The management of technical debt ultimately requires decision making – about incurring, paying off, or deferring technical debt instances. This position paper discusses several existing approaches to complex decision making, and suggests that exploring their applicability to technical debt decision making would be a worthwhile subject for further research. ![]() |
|
Snipes, Will |
![]() Will Snipes, Brian Robinson, Yuepu Guo, and Carolyn B. Seaman (ABB, USA; University of Maryland in Baltimore County, USA) Making a decision about whether to fix or defer fixing a defect is important to software projects. Deferring defects accumulates a technical debt that burdens the software team and customer with a less than optimal solution. The decision to defer fixing a defect is made by Software Change Control Boards (CCBs) based on a set of decision factors. In this paper, we evaluated the set of decision factors used by two CCBs at ABB in the context of technical debt management. The aim was to determine how a model of cost and benefits of incurring technical debt could be part of the CCB decision process. We identified the cost categories and decision factors for fixing and deferring defects as a result of interviews with CCB members. We found that the decision factors could incorporate the financial aspects when using the technical debt metaphor. We identify opportunities for further research to integrate technical debt concepts with the decision factors towards better long term outcomes. ![]() |
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Spinelli, Stefano |
![]() Francesca Arcelli Fontana, Vincenzo Ferme, and Stefano Spinelli (University of Milano-Bicocca, Italy; Blue Reply, Italy) Different forms of technical debt exist that have to be carefully managed. In this paper we focus our attention on design debt, represented by code smells. We consider three smells that we detect in open source systems of different domains. Our principal aim is to give advice on which design debt has to be paid first, according to the three smells we have analyzed. Moreover, we discuss if the detection of these smells could be tailored to the specific application domain of a system. ![]() |
|
Szynkarski, Alexandra |
![]() Bill Curtis, Jay Sappidi, and Alexandra Szynkarski (CAST, USA; CAST, France) This study summarizes results of a study of Technical Debt across 745 business applications comprising 365 million lines of code collected from 160 companies in 10 industry segments. These applications were submitted to a static analysis that evaluates quality within and across application layers that may be coded in different languages. The analysis consists of evaluating the application against a repository of over 1200 rules of good architectural and coding practice. A formula for estimating Technical Debt with adjustable parameters is presented. Results are presented for Technical Debt across the entire sample as well as for different programming languages and quality factors. ![]() |
|
Vetrò, Antonio |
![]() Clemente Izurieta, Antonio Vetrò, Nico Zazworka, Yuanfang Cai, Carolyn B. Seaman, and Forrest Shull (Montana State University, USA; Politecnico di Torino, Italy; Fraunhofer CESE, USA; Drexel University, USA; University of Maryland in Baltimore County, USA) To date, several methods and tools for detecting source code and design anomalies have been developed. While each method focuses on identifying certain classes of source code anomalies that potentially relate to technical debt (TD), the overlaps and gaps among these classes and TD have not been rigorously demonstrated. We propose to construct a seminal technical debt landscape as a way to visualize and organize research on the subject. ![]() ![]() Carolyn B. Seaman, Yuepu Guo, Clemente Izurieta, Yuanfang Cai, Nico Zazworka, Forrest Shull, and Antonio Vetrò (University of Maryland in Baltimore County, USA; Montana State University, USA; Drexel University, USA; Fraunhofer CESE, USA; Politecnico di Torino, Italy) The management of technical debt ultimately requires decision making – about incurring, paying off, or deferring technical debt instances. This position paper discusses several existing approaches to complex decision making, and suggests that exploring their applicability to technical debt decision making would be a worthwhile subject for further research. ![]() |
|
Visser, Joost |
![]() Jelle de Groot, Ariadi Nugroho, Thomas Bäck, and Joost Visser (Software Improvement Group, Netherlands; Leiden University, Netherlands; Radboud University Nijmegen, Netherlands) Assessment of the economic value of software systems is useful in contexts such as capitalization on the balance sheet and due diligence prior to acquisition. Current accounting practice in determining software value is based on the cost spent in software development. This approach fails to account for the efficiency with which software has been produced or the quality of the product. This paper proposes three alternative models for determining the production value of software, based on the notions of technical debt and interest. We applied the models to 367 proprietary systems developed by a range of different organisations using a range of different programming languages. We present the valuation results and discuss the weaknesses and strengths of the models. ![]() |
|
Zazworka, Nico |
![]() Clemente Izurieta, Antonio Vetrò, Nico Zazworka, Yuanfang Cai, Carolyn B. Seaman, and Forrest Shull (Montana State University, USA; Politecnico di Torino, Italy; Fraunhofer CESE, USA; Drexel University, USA; University of Maryland in Baltimore County, USA) To date, several methods and tools for detecting source code and design anomalies have been developed. While each method focuses on identifying certain classes of source code anomalies that potentially relate to technical debt (TD), the overlaps and gaps among these classes and TD have not been rigorously demonstrated. We propose to construct a seminal technical debt landscape as a way to visualize and organize research on the subject. ![]() ![]() Carolyn B. Seaman, Yuepu Guo, Clemente Izurieta, Yuanfang Cai, Nico Zazworka, Forrest Shull, and Antonio Vetrò (University of Maryland in Baltimore County, USA; Montana State University, USA; Drexel University, USA; Fraunhofer CESE, USA; Politecnico di Torino, Italy) The management of technical debt ultimately requires decision making – about incurring, paying off, or deferring technical debt instances. This position paper discusses several existing approaches to complex decision making, and suggests that exploring their applicability to technical debt decision making would be a worthwhile subject for further research. ![]() |
|
Zhang, Jie |
![]() John D. McGregor, J. Yates Monteith, and Jie Zhang (Clemson University, USA) The members of the ecosystem encompassing our organization are affected by our decisions just as we are affected by their decisions. If an organization takes on technical debt with respect to a specific asset, that decision will affect users of the asset either directly or indirectly. In this position paper we distinguish between incurring technical debt directly and experiencing the effects of technical debt indirectly. We illustrate why two separate concepts are needed for a complete theory and provide examples from ecosystem models we have created for several organizations. The result is a model that produces good explanations for posited scenarios. ![]() |
|
Zhu, Liming |
![]() John Brondum and Liming Zhu (NICTA, Australia; University of New South Wales, Australia) Visibility of technical debt is critical. A lack thereof can lead to significant problems without adequate visibility as part of the system level decision-making processes [2]. Current approaches for analysing and monitoring architecture related debt are based on dependency analysis to detect code level violations of the software architecture [2,3,6]. However, heterogeneous environments with several systems constructed using COTS, and/or several programming languages may not offer sufficient code visibility. Other limiting factors include legal contracts, Intellectual Property Rights, and just very large systems. Secondly, the complexity of a software dependency is often greater than simple structural dependencies, including; multi-dimensional properties (as argued by [10]); behavioural dependencies [5,9]; and ‘implicit’ dependencies (i.e., dependency inter-relatedness [11]). This paper proposes a simple modelling approach for visualising dependency relationships as an extension of the current approaches, while supporting complex dependencies. The model can be built using existing dependency analysis and general architectural knowledge; thus is better suited for heterogeneous environments. We demonstrate the proposed modelling using an exemplar, and two field case studies. ![]() |
38 authors
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