Clemente Izurieta

Using technical debt data in decision making: potential decision approaches

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.

How Software Designs Decay: A Pilot Study of Pattern Evolution

There is little empirical knowledge of the effectiveness of the object-oriented paradigm. To conduct a systematic review of the literature describing empirical studies of this paradigm. We undertook a Mapping Study of the literature. 138 papers have been identified and classified by topic, form of study involved, and source. The majority of empirical studies of OO concentrate on metrics, relatively few consider effectiveness.

Comparing four approaches for technical debt identification

Software systems accumulate technical debt (TD) when short-term goals in software development are traded for long-term goals (e.g., quick-and-dirty implementation to reach a release date versus a well-refactored implementation that supports the long-term health of the project). Some forms of TD accumulate over time in the form of source code that is difficult to work with and exhibits a variety of anomalies. A number of source code analysis techniques and tools have been proposed to potentially identify the code-level debt accumulated in a system. What has not yet been studied is if using multiple tools to detect TD can lead to benefits, that is, if different tools will flag the same or different source code components. Further, these techniques also lack investigation into the symptoms of TD “interest” that they lead to. To address this latter question, we also investigated whether TD, as identified by the source code analysis techniques, correlates with interest payments in the form of increased defect- and change-proneness. Comparing the results of different TD identification approaches to understand their commonalities and differences and to evaluate their relationship to indicators of future TD “interest.” We selected four different TD identification techniques (code smells, automatic static analysis issues, grime buildup, and Modularity violations) and applied them to 13 versions of the Apache Hadoop open source software project. We collected and aggregated statistical measures to investigate whether the different techniques identified TD indicators in the same or different classes and whether those classes in turn exhibited high interest (in the form of a large number of defects and higher change-proneness). The outputs of the four approaches have very little overlap and are therefore pointing to different problems in the source code. Dispersed Coupling and Modularity violations were co-located in classes with higher defect-proneness. We also observed a strong relationship between Modularity violations and change-proneness. Our main contribution is an initial overview of the TD landscape, showing that different TD techniques are loosely coupled and therefore indicate problems in different locations of the source code. Moreover, our proxy interest indicators (change- and defect-proneness) correlate with only a small subset of TD indicators.

The evolution of FreeBSD and linux

Is the nature of Open Source Software (OSS) evolution fundamentally different from that of the traditional and commercially available software systems? Lehman and others conducted a series of empirical studies that found that traditional systems grow at a linear or sub-linear rate. A prior case study of the Linux OSS system suggests that OSS may evolve in a unique manner. Godfrey and Tu found that some aspects of Linux are growing at a super-linear rate rather than a sub-linear rate. Additional studies are necessary before drawing conclusions. Thus, we examine the evolution of FreeBSD and re-analyze the evolution of Linux, and find evidence that the growth of both systems has a linear upper bound, and thus appear to grow at similar rates to that of commercial systems. These results do not support the hypothesis that OSS systems grow at rates that exceed that of traditional systems.

Organizing the technical debt landscape

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.

A multiple case study of design pattern decay, grime, and rot in evolving software systems

Software designs decay as systems, uses, and operational environments evolve. Decay can involve the design patterns used to structure a system. Classes that participate in design pattern realizations accumulate grime—non-pattern-related code. Design pattern realizations can also rot, when changes break the structural or functional integrity of a design pattern. Design pattern rot can prevent a pattern realization from fulfilling its responsibilities, and thus represents a fault. Grime buildup does not break the structural integrity of a pattern but can reduce system testability and adaptability. This research examined the extent to which software designs actually decay, rot, and accumulate grime by studying the aging of design patterns in three successful object-oriented systems. We generated UML models from the three implementations and employed a multiple case study methodology to analyze the evolution of the designs. We found no evidence of design pattern rot in these systems. However, we found considerable evidence of pattern decay due to grime. Dependencies between design pattern components increased without regard for pattern intent, reducing pattern modularity, and decreasing testability and adaptability. The study of decay and grime showed that the grime that builds up around design patterns is mostly due to increases in coupling.

Graph-based ontology-guided data mining for D-matrix model maturation

In model-based diagnostic algorithms, it is assumed that the model is correct. If the model is incorrect, the diagnostic algorithm may diagnose the wrong fault, which can be costly and time consuming. Using past maintenance events, one should be able to make corrections to the model in order for diagnostic algorithm to correctly diagnosis faults. In this paper, a maturation approach is proposed which uses the graph-theoretic representations of Timed Failure Propagation Graph (TFPG) models and diagnostic sessions based on recently standardized diagnostic ontologies to determine statistical discrepancies between that which is expected by the models and that which has been encountered in practice. These discrepancies are then analyzed to generate recommendations for maturing the diagnostic models. Maturation recommendations include identifying new dependencies and erroneous or tenuous dependencies. 1 2

Design pattern decay: the case for class grime

Context: We investigate class grime, a form of design pattern decay, wherein classes of the pattern realization have extraneous attributes or methods, which obfuscate the intended design of a pattern. Goal: To expand the taxonomy of class grime using properties of class cohesion. Using this expanded taxonomy we explore the effect that forms of class grime have on pattern realization understandability. Method: A pilot study utilizing a formal experiment to explore the effects of class grime on design pattern understandability. The experiments used simulated injection of 8 types of class grime into design pattern realizations randomly selected from 16 design pattern types from a set of 6541 realizations from 520 distinct software systems. Results: We found that for each of the 8 identified class grime forms, understandability was negatively affected. Conclusion: This work serves as early communication of research for the validation of the extended taxonomy as well as the method of grime injection used in the experiment.

Object oriented design pattern decay: a taxonomy

Software designs decay over time. While most studies focus on decay at the system level, this research studies design decay on well understood micro architectures, design patterns. Formal definitions of design patterns provide a homogeneous foundation that can be used to measure deviations as pattern realizations evolve. Empirical studies have shown modular grime to be a significant contributor to design pattern decay. Modular grime is observed when increases in the coupling of design pattern classes occur in ways unintended by the original designer. Further research is necessary to formally categorize distinct forms of modular grime. We identify three properties of coupling relationships that are used to classify subsets of modular grime. A taxonomy is presented which uses these properties to group modular grime into six disjoint categories. Illustrative examples of grime build-up are provided to demonstrate the taxonomy. A pilot study is used to validate the taxonomy and provide initial empirical evidence of the proposed classification.

Preemptive Management of Model Driven Technical Debt for Improving Software Quality

Technical debt has been the subject of numerous studies over the last few years. To date, most of the research has concentrated on management (detection, quantification, and decision making) approaches-most performed at code and implementation levels through various static analysis tools. However, if practitioners are to adopt model driven techniques, then the management of technical debt also requires that we address this problem during the specification and architectural phases. This position paper discusses several questions that need to be addressed in order to improve the quality of software architecture by exploring the management of technical debt during modeling, and suggests various lines of research that are worthwhile subjects for further investigation.