Science of Computer Programming最新文献

Code smells tend to have an impact on software quality attributes such as reusability, maintainability, and understandability. These are code flaws that do not necessarily prevent the system from operating; rather, they increase the possibility of defects occurring in the future. Hence, to maintain code quality, code smells should be detected and corrected through refactoring. The objective of this paper is to investigate the associated risk of applying refactoring techniques and reveal the bad smells that may appear when fixing other bad smells. We conducted several controlled experiments to identify the smells that emerge after refactoring. The experiments resulted in a novel taxonomy revealing 9 bad smells that may appear as a result of fixing other bad smells. This represents the first study attempting to systematically identify and organize such refactoring-smell relationships into a taxonomy. The research results can assist developers in relating different bad smells to each other and helping them determine the refactoring technique that should be applied when certain smells are present. Moreover, the results can be beneficial in breaking the cycle of bad smells. Such knowledge tends to enhance code refactoring, which in turn will improve software quality and avoid technical debt.

Effort estimation is fundamental for the development of software projects and critical to their success. The objective of this paper is to understand how Colombian agile practitioners perform effort estimates and to identify opportunities for improvement based on these results. For this purpose, we conducted an exploratory survey study using as instrument an on-line questionnaire answered by agile practitioners with experience in effort estimation. Data was collected from 60 agile practitioners and the main findings are: (1) Agile practitioners prefer non-algorithmic estimation techniques, mainly those based on Expert Judgment. (2) Most of the respondents perceive that their estimates have a medium accuracy level; however, in most cases, no formal analysis of the accuracy level is carried out. (3) The determining effort predictors/cost drivers are characteristics of the project team (size, experience, and skills) and attributes of the software to be built (complexity, type, and domain). (4) The use of datasets for estimation is not common; proprietary datasets predominate and are used for productivity comparisons within the company. (5) Most of the results of related studies are comparable with ours; however, there are significant differences in terms of the roles involved and the techniques used in the effort estimation process. Based on the results and findings of the survey, we identified key opportunities to improve estimation accuracy through (1) software measurement standardization, (2) use of effort datasets, (3) implementation of techniques for measuring accuracy levels, and (4) knowledge management in effort estimation.

We provide a method to synthesize an LTL Assume/Guarantee (A/G) specification, or contract, as an interconnection of elements from a library, each of which is also represented by an LTL A/G contract. Our approach, based on counterexample-guided inductive synthesis, leverages an off-the-shelf model checker to reason about infinite-length counterexamples and guarantee correctness. To increase scalability, we also introduce a novel concept of specification decomposition, based on contract projections; we show how it can be used to break down our synthesis problem into several simpler tasks, without reducing the size of the solution space. We test our technique on three industry-relevant case studies.

Debugging is an important task to identify the defects in the software. Especially, logging is an important feature of a software system to record runtime information. Detailed logging allows developers to collect run-time information when they cannot use an interactive debugger, such as continuous integration and web application server cases. However, extensive logging leads to larger execution traces because few instructions can be repeated many times. In our previous work, to record detailed program behavior within limited storage space constraints, we proposed near-omniscient debugging, which is a methodology that records and visualizes an execution trace using fixed size buffers for each observed instruction. In this paper, we evaluate the effectiveness of near-omniscient debugging in recording infected states while reducing the size of execution traces. We conduct experiments on the Defects4J dataset and evaluate the effectiveness based on the completeness, trace size and runtime overhead. The result shows that near-omniscient debugging can completely record infected states for nearly 80 percent of bugs (with a buffer size of 1024 events). The size of execution traces can be reduced by a factor of one thousand for large repetitive executions.

Clone detection provides insight about replicated fragments in a code base. With the rise of multi-language code bases, new techniques addressing cross-language code clone detection enable the analysis of polyglot systems. Such techniques have not yet been applied to the mobile apps' domain, which are naturally polyglot. Native mobile app developers must synchronize their code base in at least two different programming languages. App synchronization is a difficult and time-consuming maintenance task, as features can rapidly diverge between platforms, and feature identification must be performed manually. The end goal of this work is to provide an analysis framework to reduce the impact of app synchronization. A first step in this direction consists in a structural algorithm for cross-language clone detection, called Out of Step, exploiting the idea behind enriched concrete syntax trees. Such trees are used as a common intermediate representation built from programming languages' grammars, to detect similarities between app code bases. Our technique finds code similarities with over 80% for the evaluation of language features, where Type 1-3 clones are manually injected for the analysis of both single- and cross-language cases for Kotlin and Dart. We validate the feasibility and correctness of our approach through the evaluation of the main language constructs for Kotlin and Dart. To validate the effectiveness we use a first case study detecting clones between 12 sorting algorithms across Kotlin and Dart, identifying clone similarities with a precision between 67% and 95%. Finally, we use a corpus of 144 mobile apps implemented in Kotlin and Dart, correctly identifying code similarities for the full application logic.

Compositional deadlock analysis of process networks is a well-known challenge. We propose a compositional deadlock analysis strategy for timed process networks, more specifically, those obtained from Simulink multi-rate block diagrams. We handle models with both acyclic and cyclic communication graphs. Particularly, the latter naturally happens in Simulink models with feedback, among other kinds of cycles. Since there is no general solution to analyse cyclic models in a compositional way, we explore the use of behavioural patterns that allow the verification to be carried out in a compositional fashion. We represent process networks in tock-CSP, a dialect of CSP that allows modelling time aspects using a special tock event. The verification approach is implemented as a new package in CSP-Prover, a theorem prover for CSP which is itself implemented in Isabelle/HOL. To illustrate the overall approach and, particularly, how it can scale, we consider several variations of an actuation system with increasing complexity. We show that the examples are instances of the client/server and the asynchronous dynamic timed behaviour patterns. These patterns and all verification steps are formalised using CSP-Prover.

Context

Effort estimation based on user stories plays a pivotal role in agile software development, where accurate predictions of project efforts are vital for success. While various supervised ML tools attempt to estimate effort, the prevalence of estimation errors presents significant challenges, as evidenced by the CHAOS report by the Standish Group, which highlights incorrect estimations contributing to a substantial percentage of failed agile projects.

Objectives

This research delves into the domain of user story-based effort estimation in agile software development, aiming to explore the issues arising from inaccurate estimations. The primary goal is to uncover these issues comprehensively and propose potential solutions, thus enhancing the efficacy of the user story-based estimation method.

Methods

To achieve the research objectives, a systematic literature review (SLR) is conducted, surveying a wide range of sources to gather insights into issues surrounding user story-based effort estimation. The review encompasses diverse estimation methods, user story attributes, and the array of challenges that can result from inaccurate estimations.

Results

The SLR reveals a spectrum of issues undermining the accuracy of user story-based effort estimation. It identifies internal factors like communication, team expertise, and composition as crucial determinants of estimation reliability. Consistency in user stories, technical complexities, and task engineering practices also emerge as significant contributors to estimation inaccuracies. The study underscores the interconnectedness of these issues, emphasizing the need for a standardized protocol to minimize inaccuracies and enhance estimation precision.

Conclusion

In light of the findings, it becomes evident that addressing the multi-dimensional factors influencing user story-based effort estimation is imperative for successful agile software development. The study underscores the interplay of various aspects, such as team dynamics, task complexity, and requirement engineering, in achieving accurate estimations. By recognizing these challenges and implementing recommended solutions, software development processes can avoid failures and enhance their prospects of success in the agile paradigm.

Code smell detection is one of the essential tasks in the field of software engineering. Identifying whether a code snippet has a code smell is subjective and varies by programming language, developer, and development method. Moreover, developers tend to focus on code smells that have a real impact on development and ignore insignificant ones. However, existing static code analysis tools and code smell detection approaches exhibit a high false positive rate in detecting code smells, which makes insignificant smells drown out those smells that developers value. Therefore, accurately reporting those actionable code smells that developers tend to spend energy on refactoring can prevent developers from getting lost in the sea of smells and improve refactoring efficiency. In this paper, we aim to detect actionable code smells that developers tend to refactor. Specifically, we first collect actionable and non-actionable code smells from projects with numerous historical versions to construct our datasets. Then, we propose a dual-stream model for fusion learning of code metrics and code semantics to detect actionable code smells. On the one hand, code metrics quantify the code's structure and even some rules or patterns, providing fundamental information for detecting code smells. On the other hand, code semantics encompass information about developers' refactoring tendencies, which prove valuable in detecting actionable code smells. Extensive experiments show that our approach can detect actionable code smells more accurately compared to existing approaches.

Quantum computing is a revolutionary paradigm in computer science based on the principles of quantum mechanics. It has the potential to solve problems that are currently unsolvable for classical computing. Applications of quantum computing already span a variety of sectors.

Ongoing enhancements to the integrated programming and development environment simplify the creation and optimization of quantum algorithms. Ultimately, the focus on supporting tools represents the starting point towards achieving quantum computing maturity, facilitating its transition from an experimental domain to a practical industry.

As quantum software gains ground and relevance in various domains, it is essential to address the evaluation of hybrid systems that combine classical and quantum elements to ensure diverse quality characteristics. However, in the realm of quantum software, models, metrics, and tools are still to be established.

The primary contribution of this paper is to present the first technological environment for measuring and evaluating the analyzability of hybrid software.

Real-world examples of hybrid software are provided to showcase the functionality of the different tools in the environment, yielding readable and representative results for the evaluator.

ChatGPT is a language model with artificial intelligence (AI) capabilities that has found utility across various sectors. Given its impact, we conducted two empirical studies to assess the potential and limitations of ChatGPT and other AI tools in software development. In the first study, we evaluated ChatGPT 3.5′s effectiveness in generating code for 180 coding problems from LeetCode, an online coding interview preparation platform. Our findings suggest that ChatGPT 3.5 is more effective in solving easy and medium coding problems but less reliable for harder problems. Further, ChatGPT 3.5 is somewhat more effective at coding problems with higher popularity scores. In the second study, we administered a questionnaire (N = 99) to programmers to gain insights into their views on ChatGPT and other AI tools. Our findings indicate that programmers use AI tools for various tasks, such as generating boilerplate code, explaining complex code, and conducting research. AI tools also help programmers to become more productive by creating better-performing, shorter, and more readable code, among other benefits. However, AI tools can sometimes misunderstand requirements and generate erroneous code. While most programmers are not currently concerned about AI tools replacing them, they are apprehensive about what the future may hold. Our research has also revealed associations between AI tool usage, trust, perceived productivity, and job security threats caused by the tools.