What makes a code review trustworthy?

Nelson, Stacy; Schumann, Johann

doi:10.1109/hicss.2004.1265711

Cited by 15 publications

(15 citation statements)

References 8 publications

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“…Since that time it has played an important role in the life cycle of numerous projects. When done properly, it may help spreading knowledge among developers and encouraging the emergence of code standards for the project (Nelson and Schumann 2004). It also affects the final quality of the project as better code review coverage results in less post-release defects (McIntosh et al 2014).…”

Section: Background and Related Workmentioning

confidence: 99%

“…According to the law, a reviewer who scrutinizes a code lying outside his/her knowledge focuses on unimportant details (Rigby and Storey 2011). Furthermore, according to Nelson and Schumann (2004), it is difficult for all reviewers to spot performance problems, race conditions or synchronization issues. Fortunately, some automated tools may aid developers in such cases (Nelson and Schumann 2004).…”

Section: Background and Related Workmentioning

confidence: 99%

“…They help to obtain a high quality code, better adjusted test coverage, consistent adherence to language standards and lower number of bugs. The knowledge about the project can be reinforced by code reviews (Nelson and Schumann 2004). However peer code reviews are time-consuming activities that require good cooperation of both the reviewer and the author of the reviewed code.…”

Section: Introductionmentioning

confidence: 99%

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Profile based recommendation of code reviewers

2017

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Code reviews consist in proof-reading proposed code changes in order to find their shortcomings such as bugs, insufficient test coverage or misused design patterns. Code reviews are conducted before merging submitted changes into the main development branch. The selection of suitable reviewers is crucial to obtain the high quality of reviews. In this article we present a new method of recommending reviewers for code changes. This method is based on profiles of individual programmers. For each developer we maintain his/her profile. It is the multiset of all file path segments from commits reviewed by him/her. It will get updated when he/she presents a new review. We employ a similarity function between such profiles and change proposals to be reviewed. The programmer whose profile matches the change most is recommended to become the reviewer. We performed an experimental comparison of our method against state-of-the-art techniques using four large open-source projects. We obtained improved results in terms of classification metrics (precision, recall and F-measure) and performance (we have lower time and space complexity).

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Profile based recommendation of code reviewers

2017

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“…Examples of such mistakes include use of undeclared variables, use of un-initialized variables, out-ofbound array referencing, wrong input of formal parameters in subroutine calls, use of inconsistent data type, and performing mixed-mode computations. A rich body of checklists, coding standards, and literature about this topic has been published [20]. Code review also examines the code to verify that comments are well used throughout and that the program contains no dead or unreachable code.…”

Section: Tools For Automated Static Analysismentioning

confidence: 99%

Development of Advanced Verification and Validation Procedures and Tools for the Certification of Learning Systems in Aerospace Applications

et al. 2005

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Adaptive control technologies that incorporate learning algorithms have been proposed to enable automatic flight control and vehicle recovery, autonomous flight, and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments. In order for adaptive control systems to be used in safety-critical aerospace applications, they must be proven to be highly safe and reliable. Rigorous methods for adaptive software verification and validation must be developed to ensure that control system software failures will not occur. Of central importance in this regard is the need to establish reliable methods that guarantee convergent learning, rapid convergence (learning) rate, and algorithm stability. This paper presents the major problems of adaptive control systems that use learning to improve performance. The paper then presents the major procedures and tools presently developed or currently being developed to enable the verification, validation, and ultimate certification of these adaptive control systems. These technologies include the application of automated program analysis methods, techniques to improve the learning process, analytical methods to verify stability, methods to automatically synthesize code, simulation and test methods, and tools to provide on-line software assurance.

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“…Typical examples include buffer overrun errors, uninitialized variables, or synchronisation problems in distributed system. Table 1 shows a list of important coding error classes for safety-critical software (from [5]). For each error class, the importance to find such bugs (proportional to the risk), as well as the difficulty to locate such errors in the text is given.…”

Section: Errors and Risks In Autonomous Systemsmentioning

confidence: 99%

Autonomy Software: V& V Challenges and Characteristics

Schumann

Visser

2006 IEEE Aerospace Conference

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Abstract-The successful operation of unmanned air vehicles requires software with a high degree of autonomy. Only if high level functions can be carried out without human control and intervention, complex missions in a changing and potentially unknown environment can be carried out successfully.Autonomy software is highly mission and safety critical: failures, caused by flaws in the software cannot only jeopardize the mission, but could also endanger human life (e.g., a crash of an UAV in a densely populated area). Due to its large size, high complexity, and use of specialized algorithms (planner, constraint-solver, etc.)g autonomy software poses specific challenges for its verification, validation, and certification.---we have carried out a survey among researchers aid scientists at NASA to study these issues. In this paper, we will present major results of this study, discussing the broad spectrum.of notions and characteristics of autonomy software and its challenges for design and development. A main focus of this survey was to evaluate verification and validation (V&V) issues and challenges, compared to the development of "traditional" safety-critical software. We will discuss important issues in V&V of autonomous software and advanced V&V tools which can help to mitigate software risks. Results of this survey will help to identify and understand safety concerns in autonomy software and will lead to improved strategies for mitigation of these risks.

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What makes a code review trustworthy?

Cited by 15 publications

References 8 publications

Profile based recommendation of code reviewers

Profile based recommendation of code reviewers

Development of Advanced Verification and Validation Procedures and Tools for the Certification of Learning Systems in Aerospace Applications

Autonomy Software: V& V Challenges and Characteristics

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