http://dl.acm.org/citation.cfm?id=3461694 http://dl.acm.org/citation.cfm?id=3461694 Sun, 31 Oct 2021 00:00:00 GMT 3461694 http://dl.acm.org/citation.cfm?id=3464305
Context: Evaluating software architectures in uncertain environments raises new challenges, which require continuous approaches. We define continuous evaluation as multiple evaluations of the software architecture that begins at the early stages of the development and is periodically and repeatedly performed throughout the lifetime of the software system. Numerous approaches have been developed for continuous evaluation; to handle dynamics and uncertainties at run-time, over the past years, these approaches are still very few, limited, and lack maturity. Objective: This review surveys efforts on architecture evaluation and provides a unified terminology and perspective on the subject. Method: We conducted a systematic literature review to identify and analyse architecture evaluation approaches for uncertainty including continuous and non-continuous, covering work published between 1990–2020.]]> Tue, 03 Aug 2021 00:00:00 GMT 3464305 http://dl.acm.org/citation.cfm?id=3464939
Modern systems, such as cyber-physical systems, often consist of multiple products within/across product lines communicating with each other through information networks. Consequently, their runtime behaviors are influenced by product configurations and networks. Such systems play a vital role in our daily life; thus, ensuring their correctness by thorough testing becomes essential. However, testing these systems is particularly challenging due to a large number of possible configurations and limited available resources. Therefore, it is important and practically useful to test these systems with specific configurations under which products will most likely fail to communicate with each other. Motivated by this, we present a search-based configuration recommendation (SBCR) approach to recommend faulty configurations for the system under test (SUT) based on cross-product line (CPL) rules.]]> Tue, 03 Aug 2021 00:00:00 GMT 3464939 http://dl.acm.org/citation.cfm?id=3447245
Open source software projects often rely on package management systems that help projects discover, incorporate, and maintain dependencies on other packages, maintained by other people. Such systems save a great deal of effort over ad hoc ways of advertising, packaging, and transmitting useful libraries, but coordination among project teams is still needed when one package makes a breaking change affecting other packages. Ecosystems differ in their approaches to breaking changes, and there is no general theory to explain the relationships between features, behavioral norms, ecosystem outcomes, and motivating values. We address this through two empirical studies. In an interview case study, we contrast Eclipse, NPM, and CRAN, demonstrating that these different norms for coordination of breaking changes shift the costs of using and maintaining the software among stakeholders, appropriate to each ecosystem’s mission.]]> Fri, 23 Jul 2021 00:00:00 GMT 3447245 http://dl.acm.org/citation.cfm?id=3447247
Background. Self-admitted technical debt (SATD) is a special kind of technical debt that is intentionally introduced and remarked by code comments. Those technical debts reduce the quality of software and increase the cost of subsequent software maintenance. Therefore, it is necessary to find out and resolve these debts in time. Recently, many automatic approaches have been proposed to identify SATD. Problem. Popular IDEs support a number of predefined task annotation tags for indicating SATD in comments, which have been used in many projects. However, such clear prior knowledge is neglected by existing SATD identification approaches when identifying SATD. Objective. We aim to investigate how far we have really progressed in the field of SATD identification by comparing existing approaches with a simple approach that leverages the predefined task tags to identify SATD.]]> Fri, 23 Jul 2021 00:00:00 GMT 3447247 http://dl.acm.org/citation.cfm?id=3450492
Object sensitivity is widely used as a context abstraction for computing the points-to information context-sensitively for object-oriented programming languages such as Java. Due to the combinatorial explosion of contexts in large object-oriented programs, k-object-sensitive pointer analysis (under k-limiting), denoted k-obj, is often inefficient even when it is scalable for small values of k, where k ⩽ 2 holds typically. A recent popular approach for accelerating k-obj trades precision for efficiency by instructing k-obj to analyze only some methods in a program context-sensitively, determined heuristically by a pre-analysis. In this article, we investigate how to develop a fundamentally different approach, Eagle, for designing a pre-analysis that can make k-obj run significantly faster while maintaining its precision.]]> Fri, 23 Jul 2021 00:00:00 GMT 3450492 http://dl.acm.org/citation.cfm?id=3450968
Abstract State Machine (ASM) theory is a well-known state-based formal method. As in other state-based formal methods, the proposed specification languages for ASMs still lack easy-to-comprehend abstractions to express structural and behavioral aspects of specifications. Our goal is to investigate object-oriented abstractions such as interfaces and traits for ASM-based specification languages. We report on a controlled experiment with 98 participants to study the specification efficiency and effectiveness in which participants needed to comprehend an informal specification as problem (stimulus) in form of a textual description and express a corresponding solution in form of a textual ASM specification using either interface or trait syntax extensions.]]> Fri, 23 Jul 2021 00:00:00 GMT 3450968 http://dl.acm.org/citation.cfm?id=3450503
Software engineers are crowdsourcing answers to their everyday challenges on Q&A forums (e.g., Stack Overflow) and more recently in public chat communities such as Slack, IRC, and Gitter. Many software-related chat conversations contain valuable expert knowledge that is useful for both mining to improve programming support tools and for readers who did not participate in the original chat conversations. However, most chat platforms and communities do not contain built-in quality indicators (e.g., accepted answers, vote counts). Therefore, it is difficult to identify conversations that contain useful information for mining or reading, i.e., conversations of post hoc quality. In this article, we investigate automatically detecting developer conversations of post hoc quality from public chat channels.]]> Fri, 23 Jul 2021 00:00:00 GMT 3450503 http://dl.acm.org/citation.cfm?id=3456873
As data volume and complexity grow at an unprecedented rate, the performance of data manipulation programs is becoming a major concern for developers. In this article, we study how alternative API choices could improve data manipulation performance while preserving task-specific input/output equivalence. We propose a lightweight approach that leverages the comparative structures in Q&A sites to extracting alternative implementations. On a large dataset of Stack Overflow posts, our approach extracts 5,080 pairs of alternative implementations that invoke different data manipulation APIs to solve the same tasks, with an accuracy of 86%. Experiments show that for 15% of the extracted pairs, the faster implementation achieved >10x speedup over its slower alternative.]]> Fri, 23 Jul 2021 00:00:00 GMT 3456873 http://dl.acm.org/citation.cfm?id=3461011
Software architectural designs are usually changed over time to support emerging technologies and to adhere to new principles. Architectural migration is an important activity that helps to transform the architectural styles applied during a system’s design with the result of modernising the system. If not performed correctly, this process could lead to potential system failures. This article presents an automated approach to refactoring architectural design and to planning the evolution process. With our solution, the architectural design can be refactored, ensuring that system functionality is preserved. Furthermore, the architectural migration process allows the system to be safely and incrementally transformed. We have evaluated our approach with five real-world software applications.]]> Fri, 23 Jul 2021 00:00:00 GMT 3461011 http://dl.acm.org/citation.cfm?id=3464938
Organizations are increasingly adopting Agile frameworks for their internal software development. Cost reduction, rapid deployment, requirements and mental model alignment are typical reasons for an Agile transformation. This article presents an in-depth field study of a large-scale Agile transformation in a mission-critical environment, where stakeholders’ commitment was a critical success factor. The goal of such a transformation was to implement mission-oriented features, reducing costs and time to operate in critical scenarios. The project lasted several years and involved over 40 professionals. We report how a hierarchical and plan-driven organization exploited Agile methods to develop a Command & Control (C2) system. Accordingly, we first abstract our experience, inducing a success model of general use for other comparable organizations by performing a post-mortem study.]]> Fri, 23 Jul 2021 00:00:00 GMT 3464938 http://dl.acm.org/citation.cfm?id=3447876
AIOps (Artificial Intelligence for IT Operations) leverages machine learning models to help practitioners handle the massive data produced during the operations of large-scale systems. However, due to the nature of the operation data, AIOps modeling faces several data splitting-related challenges, such as imbalanced data, data leakage, and concept drift. In this work, we study the data leakage and concept drift challenges in the context of AIOps and evaluate the impact of different modeling decisions on such challenges. Specifically, we perform a case study on two commonly studied AIOps applications: (1) predicting job failures based on trace data from a large-scale cluster environment and (2) predicting disk failures based on disk monitoring data from a large-scale cloud storage environment.]]> Fri, 23 Jul 2021 00:00:00 GMT 3447876 http://dl.acm.org/citation.cfm?id=3453478
Enabled by a rich ecosystem of Machine Learning (ML) libraries, programming using learned models, i.e., Software-2.0, has gained substantial adoption. However, we do not know what challenges developers encounter when they use ML libraries. With this knowledge gap, researchers miss opportunities to contribute to new research directions, tool builders do not invest resources where automation is most needed, library designers cannot make informed decisions when releasing ML library versions, and developers fail to use common practices when using ML libraries. We present the first large-scale quantitative and qualitative empirical study to shed light on how developers in Software-2.0 use ML libraries, and how this evolution affects their code.]]> Fri, 23 Jul 2021 00:00:00 GMT 3453478 http://dl.acm.org/citation.cfm?id=3464689
Commit messages recorded in version control systems contain valuable information for software development, maintenance, and comprehension. Unfortunately, developers often commit code with empty or poor quality commit messages. To address this issue, several studies have proposed approaches to generate commit messages from commit diffs. Recent studies make use of neural machine translation algorithms to try and translate git diffs into commit messages and have achieved some promising results. However, these learning-based methods tend to generate high-frequency words but ignore low-frequency ones. In addition, they suffer from exposure bias issues, which leads to a gap between training phase and testing phase. In this article, we propose CoRec to address the above two limitations.]]> Fri, 23 Jul 2021 00:00:00 GMT 3464689 http://dl.acm.org/citation.cfm?id=3446211
Satisfiability modulo theories (SMT) solvers have been widely applied as the reasoning engine for diverse software analysis and verification technologies. The efficiency of the SMT solver has significant effects on the performance of these technologies. However, current SMT solvers are designed for the general purpose of constraint solving. Lots of useful knowledge of programs cannot be utilized during SMT solving. As a result, the SMT solver may spend much effort to explore redundant search space. In this article, we propose a novel approach to utilizing control-flow knowledge in SMT solving. With this technique, the search space can be considerably reduced, and the efficiency of SMT solving is observably improved.]]> Mon, 10 May 2021 00:00:00 GMT 3446211 http://dl.acm.org/citation.cfm?id=3447246
When applying formal verification to a system that interacts with the real world, we must use a model of the environment. This model represents an abstraction of the actual environment, so it is necessarily incomplete and hence presents an issue for system verification. If the actual environment matches the model, then the verification is correct; however, if the environment falls outside the abstraction captured by the model, then we cannot guarantee that the system is well behaved. A solution to this problem consists in exploiting the model of the environment used for statically verifying the system’s behaviour and, if the verification succeeds, using it also for validating the model against the real environment via runtime verification.]]> Mon, 10 May 2021 00:00:00 GMT 3447246 http://dl.acm.org/citation.cfm?id=3447265
Software changes constantly, because developers add new features or modifications. This directly affects the effectiveness of the test suite associated with that software, especially when these new modifications are in a specific area that no test case covers. This article tackles the problem of generating a high-quality test suite to cover repeatedly a given point in a program, with the ultimate goal of exposing faults possibly affecting the given program point. Both search-based software testing and constraint solving offer ready, but low-quality, solutions to this: Ideally, a maximally diverse covering test set is required, whereas search and constraint solving tend to generate test sets with biased distributions.]]> Mon, 19 Apr 2021 00:00:00 GMT 3447265