Code Quality

Code smells are indicators of deeper design problems that may cause difficulties in the evolution of a software system. This paper investigates the capabil- ity of twelve code smells to reflect actual maintenance problems. Four medium-sized systems with equivalent functionality but dissimilar design were examined for code smells. Three change requests were implemented on the systems by six software de- velopers, each of them working for up to four weeks. During that period, we recorded problems faced by developers and the associated Java files on a daily basis. We developed a binary logistic regression model, with “problematic file” as the depen- dent variable. Twelve code smells, file size, and churn constituted the independent variables. We found that violation of the Interface Segregation Principle (a.k.a. ISP violation) displayed the strongest connection with maintenance problems. Analysis of the nature of the problems, as reported by the developers in daily interviews and think-aloud sessions, strengthened our view about the relevance of this code smell. We observed, for example, that severe instances of problems relating to change propagation were associated with ISP violation. Based on our results, we recommend that code with ISP violation should be considered potentially problematic and be prioritized for refactoring.

When changing source code, developers sometimes update the associated comments of the code (a consistent update), while at other times they do not (an inconsistent update). Similarly, developers sometimes only update a comment without its associated code (an inconsistent update). The relationship of such comment update practices and software bugs has never been explored empirically. While some (in)consistent updates might be harmless, software engineering folklore warns of the risks of inconsistent updates between code and comments, because these updates are likely to lead to out-of-date comments, which in turn might mislead developers and cause the introduction of bugs in the future. In this paper, we study comment update practices in three large open-source systems written in C (FreeBSD and PostgreSQL) and Java (Eclipse). We find that these practices can better explain and predict future bugs than other indicators like the number of prior bugs or changes. Our findings suggest that inconsistent changes are not necessarily correlated with more bugs. Instead, a change in which a function and its comment are suddenly updated inconsistently, whereas they are usually updated consistently (or vice versa), is risky (high probability of introducing a bug) and should be reviewed carefully by practitioners.

Static single assignment (SSA) form is an intermediate program representation in which many code optimizations can be performed with fast and easy-to-implement algorithms. However, some of these optimizations create situations where the SSA variables arising from the same original variable now have overlapping live ranges. This complicates the translation out of SSA code into standard code. There are three issues to consider: correctness, code quality (elimination of copies), and algorithm efficiency (speed and memory footprint). Briggs et al. proposed patches to correct the initial approach of Cytron et al. A cleaner and more general approach was proposed by Sreedhar et al., along with techniques to reduce the number of generated copies. We propose a new approach based on coalescing and a precise view of interferences, in which correctness and optimizations are separated. Our approach is provably correct and simpler to implement, with no patches or particular cases as in previous solutions, while reducing the number of generated copies. Also, experiments with SPEC CINT2000 show that it is 2x faster and 10x less memory-consuming than the Method III of Sreedhar et al., which makes it suitable for just-in-time compilation. I. I SSA form [1] is a popular intermediate code representation used in modern compilers. Each variable is defined once and φ-functions are used to merge the values at join points of the control flow graph. The properties of the underlying dominance tree [2] and the implied use-def chains make possible the use of efficient, simple, and fast algorithms for various code optimizations in SSA. However, designing a correct algorithm and developing a bug-free implementation to go out of general SSA is not so easy, especially when taking into account critical edges, branches that define variables, register renaming constraints, and the natural semantics of φ-functions as parallel copies. Some compilers restrict SSA to CSSA [3] (conventional SSA), as going out of it is straightforward. In CSSA, all SSA variables connected (possibly by transitivity) by φ-functions have non-overlapping live ranges. They can thus be all replaced by the same name without changing the semantics of the code, like with code obtained just after SSA construction. However, restricting to CSSA means either disabling many SSA optimizations (as in GCC or Jikes 1) with a potential loss in code quality, or pushing the burden on the SSA optimizations designer, who must guarantee that CSSA and register renaming constraints are correctly maintained. A

Studies show that software developers often either misuse exception handling features or use them inefficiently, and such a practice may lead an undergoing software project to a fragile, insecure and non-robust application system. In this paper, we propose a context-aware code recommendation approach that recommends exception handling code examples from a number of popular open source code repositories hosted at GitHub. It collects the code examples exploiting GitHub code search API, and then analyzes, filters and ranks them against the code under development in the IDE by leveraging not only the structural (i.e., graph-based) and lexical features but also the heuristic quality measures of exception handlers in the examples. Experiments with 4,400 code examples and 65 exception handling scenarios as well as comparisons with four existing approaches show that the proposed approach is highly promising.

Unit tests and production code are intrinsically connected. A class that is easy to test usually presents desirable characteristics, such as low coupling and high cohesion. Thus, finding hard-to-test classes may help developers identify problematic code. Many different test feedbacks that warn developers about problematic code were already catalogued. In this paper, we argue that analyzing assert instructions in unit tests also aid in identifying and reasoning about potentially problematic pieces of code. We report an analysis conducted with both open source and industry projects relating assert instructions in a unit test with quality measures of the code being tested. We observed that when a production method has a unit test that uses the "assert" instruction in more than one object instance, it often exhibits higher cyclomatic complexity, number of lines of code, or higher number of method invocations. It means that developers should monitor the number of asserts in a unit test as it may indicate problems in the production code.

Background: Network science is the set of mathematical frameworks, models, and measures that are used to understand a complex system modeled as a network composed of nodes and edges. The nodes of a network represent entities and the edges represent relationships between these entities. Network science has been used in many research works for mining human interaction during different phases of software engineering (SE). Objective: The goal of this study is to identify, review, and analyze the published research works that used network analysis as a tool for understanding the human collaboration on different levels of software development. This study and its findings are expected to be of benefit for software engineering practitioners and researchers who are mining software repositories using tools from network science field. Method: We conducted a systematic literature review, in which we analyzed a number of selected papers from different digital libraries based on inclusion and exclusion criteria. Results: We identified 35 primary studies (PSs) from four digital libraries, then we extracted data from each PS according to a predefined data extraction sheet. The results of our data analysis showed that not all of the constructed networks used in the PSs were valid as the edges of these networks did not reflect a real relationship between the entities of the network. Additionally, the used measures in the PSs were in many cases not suitable for the used networks. Also, the reported analysis results by the PSs were not, in most cases, validated using any statistical model. Finally, many of the PSs did not provide lessons or guidelines for software practitioners that can improve the software engineering practices. Conclusion: Although employing network analysis in mining developers' collaboration showed some satisfactory results in some of the PSs, the application of network analysis needs to be conducted more carefully. That is said, the constructed network should be representative and meaningful, the used measure needs to be suitable for the context, and the validation of the results should be considered. More and above, we state some research gaps, in which network science can be applied, with some pointers to recent advances that can be used to mine collaboration networks.

Background: Network science is the set of mathematical frameworks, models, and measures that are used to understand a complex system modeled as a network composed of nodes and edges. The nodes of a network represent entities and the edges represent relationships between these entities. Network science has been used in many research works for mining human interaction during different phases of software engineering (SE). Objective: The goal of this study is to identify, review, and analyze the published research works that used network analysis as a tool for understanding the human collaboration on different levels of software development. This study and its findings are expected to be of benefit for software engineering practitioners and researchers who are mining software repositories using tools from network science field. Method: We conducted a systematic literature review, in which we analyzed a number of selected papers from different digital libraries based on inclusion and exclusion criteria. Results: We identified 35 primary studies (PSs) from four digital libraries, then we extracted data from each PS according to a predefined data extraction sheet. The results of our data analysis showed that not all of the constructed networks used in the PSs were valid as the edges of these networks did not reflect a real relationship between the entities of the network. Additionally, the used measures in the PSs were in many cases not suitable for the used networks. Also, the reported analysis results by the PSs were not, in most cases, validated using any statistical model. Finally, many of the PSs did not provide lessons or guidelines for software practitioners that can improve the software engineering practices. Conclusion: Although employing network analysis in mining developers' collaboration showed some satisfactory results in some of the PSs, the application of network analysis needs to be conducted more carefully. That is said, the constructed network should be representative and meaningful, the used measure needs to be suitable for the context, and the validation of the results should be considered. More and above, we state some research gaps, in which network science can be applied, with some pointers to recent advances that can be used to mine collaboration networks.

Code smells are manifestations of design flaws that can degrade code maintainability. As such, the existence of code smells seems an ideal indicator for maintainability assessments. However, to achieve comprehensive and accurate evaluations based on code smells, we need to know how well they reflect factors affecting maintainability. After identifying which maintainability factors are reflected by code smells and which not, we can use complementary means to assess the factors that are not addressed by smells. This paper reports on an empirical study that investigates the extent to which code smells reflect factors affecting maintainability that have been identified as important by programmers. We consider two sources for our analysis: (1) expert-based maintainability assessments of four Java systems before they entered a maintenance project, and (2) observations and interviews with professional developers who maintained these systems during 14 working days and implemented a number of change requests.

Code reviews play an important and successful role in modern software development. But usually they happen only once before new code is merged into the main branch. We present a concept which helps developers to continuously give feedback on their source code directly in the integrated development environment (IDE) by using the metaphor of social networks. is reduces context switches for developers, improves the software development process and allows to give feedback to developers of external libraries and frameworks.

Abstract{Besides high code quality, a primary issue in embedded code generation is retargetability of code generators. This paper presents techniques for automatic generation of code selectors from externally speci ed processor models. In contrast to previous work, our retargetable compiler Record does not require toolspeci c modelling formalisms, but starts from general HDL processor models. From an HDL model, all processor aspects needed for code generation are automatically derived. As demonstrated by experimental results, short turnaround times for retargeting are achieved, which permits to study the HW/SW trade-o between processor architectures and program execution speed.